diff --git a/hyperpars/ppo-caribou.yml b/hyperpars/ppo-caribou.yml
index b3aa2a7..62faca4 100644
--- a/hyperpars/ppo-caribou.yml
+++ b/hyperpars/ppo-caribou.yml
@@ -1,12 +1,47 @@
 # stable-baselines3 configuration
 
+# algo: "PPO"
+# env_id: "CaribouScipy"
+# config: {}
+# n_envs: 12
+# tensorboard: "../../../logs"
+# total_timesteps: 1000000
+# use_sde: True
+# repo: "boettiger-lab/rl4eco"
+# save_path: "../saved_agents"
+# id: "3"
+
 algo: "PPO"
+total_timesteps: 5000000
+algo_config:
+    tensorboard_log: "../../../logs"
+    #
+    policy: 'MlpPolicy'
+    # batch_size: 512
+    # gamma: 0.9999
+    # learning_rate: !!float 7.77e-05
+    # ent_coef: 0.00429
+    # clip_range: 0.1
+    # gae_lambda: 0.9
+    # max_grad_norm: 5
+    # vf_coef: 0.19
+    # use_sde: True
+    # policy_kwargs: "dict(log_std_init=-3.29, ortho_init=False, net_arch=[256, 128])"
+    # in policy_kwargs: net_arch=[400, 300]
+    # policy: 'MlpPolicy'
+    # use_sde: True
+    # policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+    # clip_range: 0.1
+
+# env
 env_id: "CaribouScipy"
 config: {}
 n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 500000
-use_sde: True
-repo: "boettiger-lab/rl4eco"
+
+# io
+repo: "cboettig/rl-ecology"
 save_path: "../saved_agents"
-id: "2"
\ No newline at end of file
+
+# misc
+# id: ""
+additional_imports: ["torch"]
\ No newline at end of file
diff --git a/hyperpars/tqc-caribou.yml b/hyperpars/tqc-caribou.yml
index bacbc98..3cf4085 100644
--- a/hyperpars/tqc-caribou.yml
+++ b/hyperpars/tqc-caribou.yml
@@ -1,12 +1,47 @@
 # stable-baselines3 configuration
 
+# algo: "TQC"
+# env_id: "CaribouScipy"
+# n_envs: 12
+# tensorboard: "/home/rstudio/logs"
+# total_timesteps: 500000
+# config: {}
+# use_sde: True
+# repo: "boettiger-lab/rl4eco"
+# save_path: "../saved_agents"
+# id: "2"
+
 algo: "TQC"
+total_timesteps: 5000000
+algo_config:
+    tensorboard_log: "../../../logs"
+    #
+    policy: 'MlpPolicy'
+    # batch_size: 512
+    # gamma: 0.9999
+    # learning_rate: !!float 7.77e-05
+    # ent_coef: 0.00429
+    # clip_range: 0.1
+    # gae_lambda: 0.9
+    # max_grad_norm: 5
+    # vf_coef: 0.19
+    # use_sde: True
+    # policy_kwargs: "dict(log_std_init=-3.29, ortho_init=False, net_arch=[256, 128])"
+    # in policy_kwargs: net_arch=[400, 300]
+    # policy: 'MlpPolicy'
+    # use_sde: True
+    # policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+    # clip_range: 0.1
+
+# env
 env_id: "CaribouScipy"
-n_envs: 12
-tensorboard: "/home/rstudio/logs"
-total_timesteps: 500000
 config: {}
-use_sde: True
-repo: "boettiger-lab/rl4eco"
+n_envs: 12
+
+# io
+repo: "cboettig/rl-ecology"
 save_path: "../saved_agents"
-id: "2"
\ No newline at end of file
+
+# misc
+# id: ""
+additional_imports: ["torch"]
\ No newline at end of file
diff --git a/notebooks/optimal-fixed-policy-cont.ipynb b/notebooks/optimal-fixed-policy-cont.ipynb
new file mode 100644
index 0000000..5faf5b6
--- /dev/null
+++ b/notebooks/optimal-fixed-policy-cont.ipynb
@@ -0,0 +1,1636 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "287ae04f-d0eb-45ae-85b3-2161c80fc115",
+   "metadata": {},
+   "source": [
+    "# Optimizing fixed policies for continuous-time system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b9691ca4-5cbc-43dd-bbb3-c385836a2c5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rl4caribou import CaribouScipy as carib\n",
+    "from rl4caribou.agents import constAction\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from plotnine import ggplot, aes, geom_point, geom_ribbon, geom_density, geom_line\n",
+    "import polars as pl\n",
+    "from skopt import gp_minimize, gbrt_minimize\n",
+    "from skopt.plots import plot_objective, plot_convergence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "ac439781-ac01-4649-8830-6e5379bc3705",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ray\n",
+    "\n",
+    "@ray.remote\n",
+    "def gen_ep_rew(manager, env):\n",
+    "    episode_reward = 0.0\n",
+    "    observation, _ = env.reset()\n",
+    "    for t in range(env.Tmax):\n",
+    "        action, _ = manager.predict(observation)\n",
+    "        observation, reward, terminated, done, info = env.step(action)\n",
+    "        episode_reward += reward\n",
+    "        if terminated or done:\n",
+    "            break\n",
+    "    return episode_reward\n",
+    "\n",
+    "def gather_stats(manager, env, N=200, return_ep_rewards=False):\n",
+    "    results = ray.get(\n",
+    "        [gen_ep_rew.remote(manager, env) for _ in range(N)]\n",
+    "    )\n",
+    "    ray.shutdown()\n",
+    "    # results = [gen_ep_rew(manager, env) for _ in range(N)]\n",
+    "    #\n",
+    "    if return_ep_rewards:\n",
+    "        return results\n",
+    "    y = np.mean(results)\n",
+    "    sigma = np.std(results)\n",
+    "    ymin = y - sigma\n",
+    "    ymax = y + sigma\n",
+    "    return y, ymin, ymax "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "710a3e0c-1965-427c-8b42-01e2eddddf8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pacifist = constAction(mortality_vec=np.array([0.0,0.0,0.0]))\n",
+    "# gather_stats(pacifist, carib())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "63db8123-9eba-41d4-bafe-264a80963ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CONFIG = {}\n",
+    "\n",
+    "def g(x):\n",
+    "    manager = constAction(x)\n",
+    "    out = gather_stats(manager, carib(config=CONFIG))\n",
+    "    return - out[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9b68f22e-a7fe-43f0-9dc6-6ccc5f8d007e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 1 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:35:28,931\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 1 ended. Evaluation done at random point.\n",
+      "Time taken: 7.2761\n",
+      "Function value obtained: 854.4719\n",
+      "Current minimum: 854.4719\n",
+      "Iteration No: 2 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:35:36,220\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 2 ended. Evaluation done at random point.\n",
+      "Time taken: 7.2402\n",
+      "Function value obtained: 807.4576\n",
+      "Current minimum: 807.4576\n",
+      "Iteration No: 3 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:35:43,438\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 3 ended. Evaluation done at random point.\n",
+      "Time taken: 7.3030\n",
+      "Function value obtained: 690.1642\n",
+      "Current minimum: 690.1642\n",
+      "Iteration No: 4 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:35:51,794\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 4 ended. Evaluation done at random point.\n",
+      "Time taken: 8.3079\n",
+      "Function value obtained: 1011.0399\n",
+      "Current minimum: 690.1642\n",
+      "Iteration No: 5 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:35:59,077\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 5 ended. Evaluation done at random point.\n",
+      "Time taken: 7.6676\n",
+      "Function value obtained: 425.6824\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 6 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:36:06,866\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 6 ended. Evaluation done at random point.\n",
+      "Time taken: 7.8028\n",
+      "Function value obtained: 824.5336\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 7 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:36:14,615\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 7 ended. Evaluation done at random point.\n",
+      "Time taken: 7.4041\n",
+      "Function value obtained: 478.3516\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 8 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:36:21,976\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 8 ended. Evaluation done at random point.\n",
+      "Time taken: 7.5963\n",
+      "Function value obtained: 945.0113\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 9 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:36:29,588\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 9 ended. Evaluation done at random point.\n",
+      "Time taken: 7.3435\n",
+      "Function value obtained: 952.3377\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 10 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:36:36,936\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 10 ended. Evaluation done at random point.\n",
+      "Time taken: 7.6181\n",
+      "Function value obtained: 682.9579\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 11 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:36:44,629\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 11 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.8691\n",
+      "Function value obtained: 616.3808\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 12 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:36:52,464\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 12 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1113\n",
+      "Function value obtained: 442.6618\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 13 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:00,564\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 13 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.8246\n",
+      "Function value obtained: 428.3955\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 14 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:08,359\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 14 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.8876\n",
+      "Function value obtained: 481.3296\n",
+      "Current minimum: 425.6824\n",
+      "Iteration No: 15 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:16,256\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 15 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0396\n",
+      "Function value obtained: 406.1676\n",
+      "Current minimum: 406.1676\n",
+      "Iteration No: 16 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:24,308\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 16 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9728\n",
+      "Function value obtained: 377.4997\n",
+      "Current minimum: 377.4997\n",
+      "Iteration No: 17 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:32,352\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 17 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1355\n",
+      "Function value obtained: 344.7973\n",
+      "Current minimum: 344.7973\n",
+      "Iteration No: 18 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:40,394\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 18 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1918\n",
+      "Function value obtained: 313.7336\n",
+      "Current minimum: 313.7336\n",
+      "Iteration No: 19 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:48,620\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 19 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9072\n",
+      "Function value obtained: 297.7170\n",
+      "Current minimum: 297.7170\n",
+      "Iteration No: 20 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:37:56,585\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 20 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1385\n",
+      "Function value obtained: 250.2561\n",
+      "Current minimum: 250.2561\n",
+      "Iteration No: 21 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:38:04,669\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 21 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9760\n",
+      "Function value obtained: 155.7173\n",
+      "Current minimum: 155.7173\n",
+      "Iteration No: 22 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:38:12,650\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 22 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.8007\n",
+      "Function value obtained: 196.2027\n",
+      "Current minimum: 155.7173\n",
+      "Iteration No: 23 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:38:20,440\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 23 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9883\n",
+      "Function value obtained: 356.1454\n",
+      "Current minimum: 155.7173\n",
+      "Iteration No: 24 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:38:28,406\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 24 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9125\n",
+      "Function value obtained: 144.7521\n",
+      "Current minimum: 144.7521\n",
+      "Iteration No: 25 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:38:36,434\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 25 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1715\n",
+      "Function value obtained: 153.4413\n",
+      "Current minimum: 144.7521\n",
+      "Iteration No: 26 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:38:44,524\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 26 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0445\n",
+      "Function value obtained: 154.4670\n",
+      "Current minimum: 144.7521\n",
+      "Iteration No: 27 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:38:52,585\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 27 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9848\n",
+      "Function value obtained: 128.6586\n",
+      "Current minimum: 128.6586\n",
+      "Iteration No: 28 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:00,597\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 28 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9924\n",
+      "Function value obtained: 133.4479\n",
+      "Current minimum: 128.6586\n",
+      "Iteration No: 29 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:08,585\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 29 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0684\n",
+      "Function value obtained: 123.6162\n",
+      "Current minimum: 123.6162\n",
+      "Iteration No: 30 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:16,660\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 30 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9680\n",
+      "Function value obtained: 102.7805\n",
+      "Current minimum: 102.7805\n",
+      "Iteration No: 31 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:25,674\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 31 ended. Search finished for the next optimal point.\n",
+      "Time taken: 9.0779\n",
+      "Function value obtained: 96.8023\n",
+      "Current minimum: 96.8023\n",
+      "Iteration No: 32 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:34,722\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 32 ended. Search finished for the next optimal point.\n",
+      "Time taken: 9.2097\n",
+      "Function value obtained: 94.4083\n",
+      "Current minimum: 94.4083\n",
+      "Iteration No: 33 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:42,904\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 33 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9212\n",
+      "Function value obtained: 93.7777\n",
+      "Current minimum: 93.7777\n",
+      "Iteration No: 34 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:50,894\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 34 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0790\n",
+      "Function value obtained: 85.1689\n",
+      "Current minimum: 85.1689\n",
+      "Iteration No: 35 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:39:58,895\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 35 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9322\n",
+      "Function value obtained: 81.9333\n",
+      "Current minimum: 81.9333\n",
+      "Iteration No: 36 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:40:07,875\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 36 ended. Search finished for the next optimal point.\n",
+      "Time taken: 9.8170\n",
+      "Function value obtained: 75.4507\n",
+      "Current minimum: 75.4507\n",
+      "Iteration No: 37 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:40:16,693\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 37 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.8212\n",
+      "Function value obtained: 63.9293\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 38 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:40:25,477\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 38 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0550\n",
+      "Function value obtained: 80.0707\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 39 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:40:33,541\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 39 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.7718\n",
+      "Function value obtained: 94.8528\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 40 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:40:42,346\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 40 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0144\n",
+      "Function value obtained: 247.2551\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 41 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:40:50,368\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 41 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0527\n",
+      "Function value obtained: 138.7606\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 42 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:40:58,393\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 42 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1034\n",
+      "Function value obtained: 73.1566\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 43 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:41:06,555\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 43 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.8306\n",
+      "Function value obtained: 76.5328\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 44 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:41:15,332\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 44 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9906\n",
+      "Function value obtained: 141.0108\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 45 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:41:23,337\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 45 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9446\n",
+      "Function value obtained: 999.3756\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 46 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:41:31,315\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 46 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0997\n",
+      "Function value obtained: 68.8695\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 47 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:41:39,458\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 47 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.2354\n",
+      "Function value obtained: 76.8423\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 48 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:41:47,649\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 48 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.2502\n",
+      "Function value obtained: 65.0447\n",
+      "Current minimum: 63.9293\n",
+      "Iteration No: 49 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:41:55,953\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 49 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.2377\n",
+      "Function value obtained: 63.9185\n",
+      "Current minimum: 63.9185\n",
+      "Iteration No: 50 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:42:05,137\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 50 ended. Search finished for the next optimal point.\n",
+      "Time taken: 9.0671\n",
+      "Function value obtained: 61.0286\n",
+      "Current minimum: 61.0286\n",
+      "CPU times: user 8min 4s, sys: 9min 38s, total: 17min 42s\n",
+      "Wall time: 6min 44s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(61.02861117378444, [0.2521079381524335, 0.0, 0.0])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "res = gp_minimize(\n",
+    "    g, \n",
+    "    [(0.0, 1.0), (0.0, 1.0), (0.0, 1.0)], \n",
+    "    n_calls = 50, \n",
+    "    verbose=True,\n",
+    ")\n",
+    "res.fun, res.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "1cc458c3-7e28-483a-9862-6c323586ec0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 19:41:41,464\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "71.11453931779327"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# -> (61.02861117378444, [0.2521079381524335, 0.0, 0.0])\n",
+    "\n",
+    "# plot_convergence(res)\n",
+    "g([0.24, 0.0, 0.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5a06bdf5-0f6c-411a-bac6-bd15e701bbfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_objective(res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d03ebf66-ee2d-45ca-bdd5-6ef0aa79ca45",
+   "metadata": {},
+   "source": [
+    "## Test solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "7193ccbe-d021-4eb8-a0b8-bd07dce76309",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'action = [0.2521079381524335, 0.0, 0.0], rew = -69.14278501475572'}, xlabel='t'>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CONFIG = {'sigma_M':0, 'sigma_B':0, 'sigma_W':0, }\n",
+    "CONFIG = {}\n",
+    "env = carib(config=CONFIG)\n",
+    "\n",
+    "null_action = - np.ones(3, dtype=np.float32)\n",
+    "high_action = 0.5 * np.ones(3, dtype=np.float32)\n",
+    "all_wolves = np.float32([0.0, 0.5, 0.0])\n",
+    "all_moose = np.float32([0.5, 0.0, 0.0])\n",
+    "an_effort = np.float32(res.x)\n",
+    "# an_effort = np.float32([0.018989021076692904, 0.3169121824404405])\n",
+    "\n",
+    "an_action = 2 * an_effort- 1\n",
+    "\n",
+    "Ms, Bs, Ws, ts, rews = [], [], [], [], []\n",
+    "a_ms, a_bs = [], []\n",
+    "\n",
+    "obs, _ = env.reset()\n",
+    "ep_rew=0\n",
+    "#\n",
+    "pop = env.population_units()\n",
+    "Ms.append(pop[0])\n",
+    "Bs.append(pop[1])\n",
+    "Ws.append(pop[2])\n",
+    "ts.append(0)\n",
+    "a_ms.append(env.parameters[\"a_M\"])\n",
+    "a_bs.append(env.parameters[\"a_B\"])\n",
+    "rews.append(ep_rew)\n",
+    "#\n",
+    "for t in range(env.Tmax):\n",
+    "    ts.append(t+1)\n",
+    "    obs, rew, term, trunc, info = env.step(an_action)\n",
+    "    pop = env.population_units()\n",
+    "    Ms.append(pop[0])\n",
+    "    Bs.append(pop[1])\n",
+    "    Ws.append(pop[2])\n",
+    "    a_ms.append(env.parameters[\"a_M\"])\n",
+    "    a_bs.append(env.parameters[\"a_B\"])\n",
+    "    ep_rew += rew\n",
+    "    rews.append(ep_rew)\n",
+    "    if term or trunc:\n",
+    "        break\n",
+    "    \n",
+    "\n",
+    "ep = pd.DataFrame({\n",
+    "    't': ts,\n",
+    "    'm': Ms,\n",
+    "    'b': Bs,\n",
+    "    'w': Ws,\n",
+    "    'rew': rews,\n",
+    "    'a_M': a_ms,\n",
+    "    'a_B': a_bs,\n",
+    "})\n",
+    "\n",
+    "ep.plot(x='t', y=['m', 'b', 'w'], title=f'action = {res.x}, rew = {ep_rew}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2e321221-4875-4322-8698-7c4855435117",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 18:44:22,765\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-68.40970542630504, -137.09041561089623, 0.27100475828616766)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ep.plot(x='t', y=['a_B'], title=f'{ep_rew}')\n",
+    "\n",
+    "manager = constAction(res.x)\n",
+    "gather_stats(manager, carib(config=CONFIG))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0cad9ca-3a27-4071-be8c-74768c9feddd",
+   "metadata": {},
+   "source": [
+    "## RL solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f997716b-ac61-4985-bfdc-1471f59f835a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stable_baselines3 import PPO\n",
+    "from sb3_contrib import TQC\n",
+    "\n",
+    "ppoAgent = PPO.load('../saved_agents/PPO-CaribouScipy', device='cpu')\n",
+    "tqcAgent = TQC.load('../saved_agents/TQC-CaribouScipy', device='cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "83a7a494-b5d3-4942-bd46-b4ad0e1efab7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': '-39.25649191800393'}, xlabel='t'>"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CONFIG = {'sigma_M':0, 'sigma_B':0, 'sigma_W':0, }\n",
+    "CONFIG = {}\n",
+    "env = carib(config=CONFIG)\n",
+    "\n",
+    "Ms, Bs, Ws, ts, rews = [], [], [], [], []\n",
+    "a_ms, a_bs = [], []\n",
+    "mculls, wculls, rests = [], [], []\n",
+    "\n",
+    "obs, _ = env.reset()\n",
+    "ep_rew=0\n",
+    "#\n",
+    "pop = env.population_units()\n",
+    "Ms.append(pop[0])\n",
+    "Bs.append(pop[1])\n",
+    "Ws.append(pop[2])\n",
+    "ts.append(0)\n",
+    "a_ms.append(env.parameters[\"a_M\"])\n",
+    "a_bs.append(env.parameters[\"a_B\"])\n",
+    "rews.append(ep_rew)\n",
+    "mculls.append(0)\n",
+    "wculls.append(0)\n",
+    "rests.append(0)\n",
+    "#\n",
+    "for t in range(env.Tmax):\n",
+    "    action, info = ppoAgent.predict(obs)\n",
+    "    ts.append(t+1)\n",
+    "    obs, rew, term, trunc, info = env.step(action)\n",
+    "    pop = env.population_units()\n",
+    "    Ms.append(pop[0])\n",
+    "    Bs.append(pop[1])\n",
+    "    Ws.append(pop[2])\n",
+    "    a_ms.append(env.parameters[\"a_M\"])\n",
+    "    a_bs.append(env.parameters[\"a_B\"])\n",
+    "    mculls.append((action[0] + 1)/2)\n",
+    "    wculls.append((action[1] + 1)/2)\n",
+    "    rests.append((action[2] + 1)/2)\n",
+    "    ep_rew += rew\n",
+    "    rews.append(ep_rew)\n",
+    "    if term or trunc:\n",
+    "        break\n",
+    "    \n",
+    "\n",
+    "ep = pd.DataFrame({\n",
+    "    't': ts,\n",
+    "    'm': Ms,\n",
+    "    'b': Bs,\n",
+    "    'w': Ws,\n",
+    "    'rew': rews,\n",
+    "    'a_M': a_ms,\n",
+    "    'a_B': a_bs,\n",
+    "    'mcull': mculls,\n",
+    "    'wcull': wculls,\n",
+    "    'restoration': rests,\n",
+    "})\n",
+    "\n",
+    "ep.plot(x='t', y=['m', 'b', 'w'], title=f'{ep_rew}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "47f2b1e7-8743-4e3a-a6f4-b04273222137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "window = 10\n",
+    "ep['wcull_moving_av'] = ep['wcull'].rolling(window=window).mean()\n",
+    "ep['mcull_moving_av'] = ep['mcull'].rolling(window=window).mean()\n",
+    "ep['rest_moving_av'] = ep['restoration'].rolling(window=window).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "a6f8b8a7-b7ef-40fc-b54f-ada9e2667d52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': '-39.25649191800393'}, xlabel='t'>"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ep.plot(\n",
+    "    x='t', \n",
+    "    y=['wcull_moving_av', 'mcull_moving_av', 'rest_moving_av'], \n",
+    "    title=f'{ep_rew}',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e26a31c0-9fed-4618-88a4-a2d5a895c6ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': '-791.6995541050107'}, xlabel='t'>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CONFIG = {'sigma_M':0, 'sigma_B':0, 'sigma_W':0, }\n",
+    "CONFIG = {}\n",
+    "env = carib(config=CONFIG)\n",
+    "\n",
+    "Ms, Bs, Ws, ts, rews = [], [], [], [], []\n",
+    "a_ms, a_bs = [], []\n",
+    "mculls, wculls, rests = [], [], []\n",
+    "\n",
+    "obs, _ = env.reset()\n",
+    "ep_rew=0\n",
+    "#\n",
+    "pop = env.population_units()\n",
+    "Ms.append(pop[0])\n",
+    "Bs.append(pop[1])\n",
+    "Ws.append(pop[2])\n",
+    "ts.append(0)\n",
+    "a_ms.append(env.parameters[\"a_M\"])\n",
+    "a_bs.append(env.parameters[\"a_B\"])\n",
+    "rews.append(ep_rew)\n",
+    "mculls.append(0)\n",
+    "wculls.append(0)\n",
+    "rests.append(0)\n",
+    "#\n",
+    "for t in range(env.Tmax):\n",
+    "    action, info = tqcAgent.predict(obs)\n",
+    "    ts.append(t+1)\n",
+    "    obs, rew, term, trunc, info = env.step(action)\n",
+    "    pop = env.population_units()\n",
+    "    Ms.append(pop[0])\n",
+    "    Bs.append(pop[1])\n",
+    "    Ws.append(pop[2])\n",
+    "    a_ms.append(env.parameters[\"a_M\"])\n",
+    "    a_bs.append(env.parameters[\"a_B\"])\n",
+    "    mculls.append((action[0] + 1)/2)\n",
+    "    wculls.append((action[1] + 1)/2)\n",
+    "    rests.append((action[2] + 1)/2)\n",
+    "    ep_rew += rew\n",
+    "    rews.append(ep_rew)\n",
+    "    if term or trunc:\n",
+    "        break\n",
+    "    \n",
+    "\n",
+    "ep = pd.DataFrame({\n",
+    "    't': ts,\n",
+    "    'm': Ms,\n",
+    "    'b': Bs,\n",
+    "    'w': Ws,\n",
+    "    'rew': rews,\n",
+    "    'a_M': a_ms,\n",
+    "    'a_B': a_bs,\n",
+    "    'mcull': mculls,\n",
+    "    'wcull': wculls,\n",
+    "    'restoration': rests,\n",
+    "})\n",
+    "\n",
+    "ep.plot(x='t', y=['m', 'b', 'w'], title=f'{ep_rew}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac568255-90f9-4552-ac44-c2050e21d97c",
+   "metadata": {},
+   "source": [
+    "#### Uhhhh... what?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "142c7538-f41a-46ce-b11f-a0c5a4e8f55f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': '-791.6995541050107'}, xlabel='t'>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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cs88+S7773e+S+vp6Eo/HSW1tLTn44INNu7zmOPXUU8mYMWNIdXU1icfjZKeddiIXXHCByQLghNNqmuXLlzvW25AhQ4rSefjhh8mECRNIMpkkAwcOJJdeeilpbW01hbnkkkvIhAkTSPfu3Uk8HieDBw8mZ511FlmzZo2tbDRpEkL/jAnJrt6ZOnUqSaVSpHfv3mTOnDmO9bTXXnuRPn36mKwzGk0pYBDisfWhRqPRaDQajUL0ahqNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIGilRGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIFSEpueZTIZrFq1CtXV1TAMI2hxNBqNRqPRUEAIwdatW9G/f/+iDR4LKQllZNWqVfnDtjQajUaj0ZQWX331FQYOHOh4vySUkerqagDZwtTU1AQsjUaj0Wg0GhoaGxsxaNCg/DjuREkoI7mpmZqaGq2MaDQajUZTYni5WGgHVo1Go9FoNIGilRGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIGilRGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIGilRGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIHCrIy89NJLOPLII9G/f38YhoFHHnnEM84LL7yAPfbYA8lkEjvttBPuuusuDlE1Go1Go9F0RZiVkaamJowfPx633HILVfjly5fj8MMPx4EHHoilS5fi3HPPxamnnopnnnmGWViNRqPRaDRdD+aD8g499FAceuih1OFvu+02DBs2DNdffz0AYJdddsErr7yC3//+95g+fTpr9tLZ0dyKjVu3ApEESLoNZPsaJCKd1UIqesFo2QIj0w7DANKVdYjs2ASDZAAYyFT2QWT7ehgGycepSsZgwEBrOoN0OoPm9jSqUnHsaG1HOpMNF4tGUJ2M49vtLUjFoohGI2hqaQMAVMZjqE7FsG5bCwghqK1MYFtLO7olYjAMYPP21qJypGJRAEBze9q1vNGIgXgkgpZ0Bj0qE9jU1AIAiBgGulcmsK2lLSt7exrVqTiqktm6IIRg/bYWpOIxpNMZZAB067jX0pZGMhZFWyYDA8COtjRihoHayjjWbs2WoTP/CKpTsXwZ4rEIqhLxfDowgJb2NKqScWzZ0dpZV6lsmG+bWhCPRVDXLYl1W1uQjEWQjEXRnsnAMAzsaGtHTTIOAoAASMUiaG7LIBWPgBCCTdvb0LMy7nhoU2t7BvGo4XmoUyGEELSkCVKxrG7f2NyOeNRARbzjmXTk39yeQSJioDVNEI8aiEYMtLRn0NyWRlUyhrYMQcwAWtIE6QxBTSpbv5uaWtGazqBnZQKJjjza0xmkCZCMZctVKG9LeyZblnQG1ckYCCFobs8gFYuAEGDT9lbUpOKIRw20pEm2ooxshUUjQDzaWWc52tMZxDqub29tR1UyhkxH/W5oakFNMo7NO1qRjEWxvbUdvbolsbWlHb26JfJ10tyWRveKONIZkn+2vbol0ZrO5OVOZwiqkjFUxCPY2pJGIhpBImogEuksX+OOdrSm04hHIkjGo9ja0gYDQF1VMl9f3ZIxkI42VdVRB+2dzTD/rAghaGknaGptR3VHmQghaM8QVCc7+4Hm9gxa2zKoqche29GWRkU8inQmGzbZ0c4iBkztZ0NTC9JpguxPA1XJGCIRAy1tadSkYmhLEyRiEWzZ0Ya2dAZVqTgSEQO5x7lxeyt6VWbrsD1DEDEMtGUI2jqebY7N29tQk4qZ6ilXPkKQv97SnkEyxjc7TwhBa5ogamT7kXSGYGtzOyqSMSQ60m/NkI52RpAhQFuGYGtzG+q6JdCeIdiwLdvfVKfiiEUjaGlNI5mIAgRoz2TQ1p5BOyGoTna879uz4Xt0S6JxRyvSGYK6qiTi0QiaWtPIkGx+W5vbkYhFYBgG4h31t7W5HT072l97OoONHe0inSaIRA1UJ2P5dp7JELR29M25PgNG9rVIxiLY1pJt8+kMwbqtzaitTKCppR1VqTgiAFrSGSSjkfz7CQBtHe9oU0s74pEIIpHs2PBtUyuqUzFEIwY2NrWiV7cE0pls/1rb8awBYFtLO5KxaLY+ke33oxED1ak4tja3oWdlAq0ZgmTUQEs7QcQAIgawvS2DHa3t6FOdxJYd7UgTkn8Pc+T6gw1NLUhEoohFDazf2oL6AYORTFVytQ9RDFI4UrBGNgw8/PDDOProox3D7Lfffthjjz1w44035q/97W9/w7nnnostW7bYxmlpaUFLS0v+d+4I4i1btkg9tXfLpvWovHkU4sgO4Bf17oWnu1Xi6a9WoW/afVDXaDQajaYrsfzoRzFswgFS02xsbET37t09x29mywgra9asQd++fU3X+vbti8bGRuzYsQMVFRVFcRYsWIArr7xStWhY+9nb2BmdSsfjVd0AAPdXV+P0b5uQMtry91pJFAkjXfA7hoTRbvqdtQtoSolELIrcx2S644szez2C9kz2qzIRi+SfbHObs5IaMQxk+HV7WwzDsFiWDBAAmYzcfGjzZyX3RS4qL40ckYgBA8hbH72IRiNIdzxvO2LRCGKR7DNt7bA2xaIRtLvEYZWZJRxLPMMwOqw12fYaj0XQ1m6WOxGLIGIY+TC530CnsQzorM82ynL7gWi7VJVXPBpBNGK49hMi6Yvi1X9sbw3uI1y5MsLDRRddhHnz5uV/5ywjsiER++JHxx+L1IG/AW7YFWj8GgCQmHEX8OpNwDeLs78vWg78ZghAOgav054FBk7EI29/g3PvX5pP68EzG7Bzn2pc9fiH2HdkHY7efQCeeHc15tz7VlG+B4/piz+fOBHDLnoyf+0Px+2OPzz/KSYP64l//m8lAGBknyo8N2//fJj2dAY/f/AdRAwD1/14PKIRZ6WotT2DxSs24fg7Xs9fW3Ht4djvt//Fyk3bcfYBI/DT74zEmf9cghc/We+YzhHj+oEQ4In3VjuGyfH4T/fB2AHdAQBfbmzCe99swSG71uOhJV/jwn+95xjv19/fDYlYBL948B3PPHh58WcH4PpnP8Hw3t2wc99qnH1P9rn86Ud75P/+28l74sDRfQAAoy98Qpksbozo3Q2fr2/CEeP6IZ0heOr9NYHI4cUfjtsdP/2/t/O/p+3SB6l4FI+/a24no+ur8fGarUXxf3HwzvjHoi+xbmtL0b1Cjp8yGFcdtSu2t6Xx0OKvcdXjH+KQXevRLRnD/3vrayll+fX3d8PxUwbj8XdW4WcdZTp/+ij87pllVPET0Qha0xm8eP4BGNKrG079+5v4z0frisINqK3AN5t3MMuXaxNOnLHfcPz5pS8AAP+YORkn3fmG6f78g8dg9t7D8m36r8dOwkG79MW7X2/GrDvfwAWHjMZh4/ph3BXPMsvmxE59qtCzWwJvLN/kGKZ7RRxbdmQ/BHtXJ5HOEGxqyk7r5crcs1sif42FJ3+2L/70wmdF7dENluczvK4b7jtjL0y+ZqHp+tVHj8Vlj7xvG2enPlX4bN0207Xzp4/CnAN3wtKvNuPoW14FAFQnY+hdk8QXLs88RzxqoC1drHAcOb4/2tozePqD4v7jyPH98YfJu3umrQrlykh9fT3Wrl1rurZ27VrU1NTYWkUAIJlMIplMqhYNBIzaaKEfQcp7umhYXTfsObQnAOD6Y8bbJlOchYF9dqrDK59tQFUyhiPH98eR4/vjoSVf55URK7FoBDcdS9eIErEIKpPFj/0fP5mMhR+vw0kNQxC3zH3aMWvqUOw5tCeqHnoX9y/+ynTP+nLFo51pDenVDUN6dXNNe9KQHjhu8mB8f/cB+Nfb39AUi5tXP9uIx95ZBQC45fg98tdNHyohMHjl6pAArtaXiAHQGAaG9+5G1amxYp2bbme0iKTiUQyt6+apjADZdl9D0VZ5ISB49bMNeUWEldZ0Bt0r4hjcMzcHb9+QWtr5LA5efk05RQSwbzM7LF/vuUd1zn1L8e32Nlz4r/fwnQ4lXBYjenfDt9vbXMP0657KKyNWYpHss97W0m5734v+tSlUp9iGvVYXi9CCH+yGHa1p3PXaCqzctB1fbGgqUkSqUzH0rnIez+ysIrk2k/M7A7LWLVoOGNUHh+xaj3+/uwovLDN/VHaz6f+B4Ls55fuMNDQ0YOFC88N57rnn0NDQoDprcQpfdsMA6na2Bij60yi+VIST4SIX94YZ4/H93Qfg3tOmsEhLjV32Q+u64ZR9huUHvUTUvWn0657KpmVJbL+de+OgXcwdGOtgMbhXJX44cWCRM54KNm7rHPQKlVNmRdVH3Mb3bgm6jtbr+XJjeWTpDHtNdktEvQPZ4JTTVd/blSs9APh3h6LKy9479corDU7NudXD6dwJFtO+nTLSbDHJ56ZjVE4ZDO9dxTToWcPm+ptWXgUORn4qiha3vCriUfxkn2HoXa3m47mw74xH6eU2APxw4kDss1Nd0b2qpP37xVgt0mHukbZt24alS5di6dKlALJLd5cuXYqVK7Nf7RdddBFOOumkfPgzzzwTX3zxBX75y1/i448/xp/+9Cc88MADOO+88+SUQASnl87poRw0Hxg5HTjufsGM3Z96n+oUfj9jAsYNrLWNIbPROKXlpUD0rUlR5+GUFk05VL8fWwu+sAqbg09TuNQUfgW7DRYVnAO5LAzLE3v50w14wsYk7vRVbxiGreWuOJ/COM7hvjumL05qGIpKjnqxq2bWgfqAUZ2KuZOcbl/ebrBIkrHJwmoZyZWNZSUZK7mPGDes+cuWxm0a2w5exSeHAfc2avccc+ELFZB4NEJdF275hdUywjxNs3jxYhx44IH53znfjlmzZuGuu+7C6tWr84oJAAwbNgxPPPEEzjvvPNx0000YOHAg7rjjjlAs6/XGMP9d3ReY+UDBJaOgJdk8Sg8LSHFwf5oDTV/j9OXcpzqJGXsOyltQrGkZKC4Hi0YPm/gq2dpcoIwUXPdDF+mWiKKJ0mEsXyPE3TJCOyuiasChTdYtWBWldceKnZ7wi4NHeebHCu1UGAD0KFiq6dSueadpWBppmmGaRuUXcu+qpGf6boqmcLs1wGwZaXGxXOWSEpLK5TkW9sM81ky7+nJURgI2jTC/9QcccIDr14Hd7qoHHHAA3n6bb95VKY7lUPtQWF8GwDr9I08+p5ScrBm/nzEBe9uY/txIRh3MghTlUP1+bDNZRojt36qoq06iaeP2ouuxiFHka1FYD24+I7JX87Ai+uVmAKh0MCM7xc+1I9klt0uPkI5Om2v1i0M+nIKzWUZspmnazEpQTmFR+cr1qfGezihWQAr+liADq2VEdOGaYbj3dLaWkY4Yhf52uZUwVHm65OjkMxO0ZUSfTeOGpy3YcL3v9HAdr/vUGmiUgLiDFu5VSsMoLkc8xmgZ8fGt2Nbs7kynEqdBqKYiXnQtVycExLVzpF1+qaqKRb+uDANIxtimVNwdwnP/yisxy1hWGJTnI8QNFoXZ1jLS6v80Te+qlGf/4/bhJWwYMdiVEdp0eXF7jixOq4W4WWyc+vagtZFQLu0tGUwt0Oi45P1EIxzty+pLKwsneWWuUHA0L9L4jPhqGVGblxUnh8uqZKxo2WJhp+zWedHusRG0s5qbZcRurLAaI+wGtOc+XFt0TQXZd4ajsQRY53bNwjpNk2s7KsWkcfQsfradv2W0W9lKoRfZDzS+DzJr36lScj+nx+0ob8uI5+hTrGzIIOiHToMsZSRiZJdghhWzz0jhNE1BIEVKilPzi9mMxnnLCHGfiglaGZGRbmEaNP5Gyt4mm3ou3AyMFdlyik/T2PuMqBqsK+JRKgdrVR9eQPYZqOiORPp0t+fI6m+Xo9MiaJOf07qNgIel8I4SJUHxW2Oe2XH5/LO77Nc0DUU+judXuMzn5m4XXnI0Cbrg5zthUkYKV9P44MLq5rI0Y9Ig66U8disjcuSUkYPH9HUOBHUKsWiq2fn1gi9hFL9XPPLILC3LYF0YVP40DX1YOyW12IE1N00jJJYjOSWbxYG1+J64cFGJBaSxeFj7RCt2zzHfbgtX0TFJVnqUtTLiPODYqJVS55zZ0/J78JC1D4WbhYWmRKqtSFsLfEbCsrTXQHa/FtO13DbdXpaRjnsxjy+qoC0jBgzbsEU+RxSKu6qyODmw8rocyJaTRWG23WfE4sDqh88IFQX5G5D7rA3DULN/kZeC5XKf5TmyvF+s+QVtry9rZUQYW58R6xWbaI7Xg24OnTiZB4scyqz3DfMgw7ccjTkKN4XLKk0zMz4oI05KhbUOs9c6/3aTjVCa2tVVsVjKWZ8RmV+uBQlLS5PPMiJ9moahjdruwNpq3sU05/usbK9Bl6kDm2DM92iRahmhCeORn61lxE5RpxOJO07QOmhZKyOOjoBG0R+wf6x8Krvw0l4VnbWFBOOKBidcLSM0Jk4fXxDT0l5f8rO/7mXWpVm+67liIOh9RgznMp7YMAQ9uyUwc8pgW3M1YH0z1ZTFdtMzEO6qC3aapvia39M0tFj7OsPpJicqLCNeKbpaRhieI3Vb58gv6I9hvZpGIY4uI6wmE8nQvM+0u6ba+4x0XuRxhA3qpTBbRnzwGXEyl1qnKoCC01SJFGVE8cevQAIG6qqSePOSaYhGjPyhd67pUtyUWV4mnxHTB4tEIRixW9pr3Vk0P02jWFDPpb2c9+jylru0l6Yp8OUmR0ZeK14QlLVlxBkbe6LTBLclDtVGXgKSyYhvTss+NVmradwcWMMwKUUcf/iQt8sXipslgGbBjExTtAqyvgDFMuauWAeM4k3ggisfb9ZBfnnaKdfWOswv7VUkJm+ysv2DVLwb3lNP4nkSinw683NPJ0f3gj2Ngu4yylsZCchL0fFcDp/lcIPW18PuJRP1GQkK80F5fuRnj90URuHSXhqrTXAOrII+I0WWNvXKvRN29Zx1YOXLUfbsANOmZzYarFWczu3gg+2JVOZvGGqmabzydMNeURTN0/BM55Bd63Hy1KGFscQyFaR0RopAYPcZMTusOSgdQc/JUjS6hMOuqaxzozw7CAZdPwB80Uac1/vbWUY6f9NYRoJyYBW3+smVrHO/BYmmec7AtCLQjpVM+4zY+r9YwxCm/FmhGSABS69rMw0sCufWHbZ0Lj1nKJQF+i3eJVHgG+THqjRayloZEfYLoFmDaBfNMTnnNArvSW00DmklHM6TYSXpNk1DM9+q+g1xWM7rzz4jzkvsikqds4xAjgNr0AflweqYKJCeqrLYLu0VyI9W0dqlXw2VQsC0moZCg807sNInqwSrIm3ad0bU8gZDyXbw3rk647bPCH+OtOEK6lYwT1HKWhnxhENtdNPqc/htJrQi5sBq6ShsRs3CS1wOrAFVj99Le52wc2Bl9RkJzjIie5qGJk/vezLbFEtahUFpj4HIPn/vTFgUZjsHVr+naegVVec4MiST2f92Wt7401D54WMnVi63oC0hVspbGRE+tZfPxuVoGaGMI7Vjdbguz4HVzdrjHd/P98VsGfEhP4frhlGsTBT+pvIZ8bSMeCbBBYuDncheCn50pLbVTIjANIZcoUV3YLUSFsuINX+ZUwmG4b9zt5dyab/PSHF4Fqsc7VJiPU1TMvBNw3imGriDmHcYp8HM60vFsIwyPNvB+9kdmp1WHc6mUYTjpmcwiqqg0IGVdprG7TkHvaeAE8VLx73ldC1nzk9BRChrmixTshzTqwZlDqJn01jlyYVRdTZNp5WKXlEu3mRRXDaplpHcv0KWEYW4Tf1bajPoPkErI3ZwfZ4ZxZdoolFct96T2WhU60VuqzqoVkools98Gm7n9SDPprFbTVMI1TRNwFOBXmTLyC9j0B0nD0E+Epo2k98YLURVW/QuSJDNd8sI2MXmsZ7Thiu0rGrLSEggcDlxDLBqAHYB7MN6EKJ3XRi3vSJKDb/dRFwdWB2naSg3PWNZsiARYTN60Zew+jydcHJg5VVUaRUoO58hO5imaSgCq15NQ4tbPckQTYUDq9CpvR6PZp+d6gAAJzUMoZeHxiJomT4Kut/WO7BKp9Asa/94ncygfjUGEYuEV8yiaRuOUvmpoZunZvydpnHMwmalSWGd0MgWiWTTEPWKYoXpedsFlSwY7dQAC2zbdxf8Lb3S6QWh8TMiRPE0DeWUmec3oJAMfFbDiGFvXaK1hLuHc59Cu2PWJHy8ZivGDeiO+974yjtDD5x91fQ0TXB4OrAWT8PYBnO675Q6xzM3L29jj0+Trt/QObD6J5/fDqxOmWQtIw5RaH1GGOblZcLiwEp1nWJKU93ZNPabnvG2De5t5B0Qd2A159G5moY+XRW4+Q3JkI1nmiamcPNGr+eYikcxYVBt9gOD8f1y9xsL/lkXoi0jkqHR6p03Q/OnZYgoAZ7Wf0oTs3vewWAa5G16iA9WbZGan1MfFDFs9loo+E3nwNoRx81JVgGym7DoNE1+6aUUabIwWUa4Pjwo5WBIk2aaJhdGWdvI/es5g2i1LhPbe7zwTNPEIwZabe90WHtcB313l2RbNVFRJ/jp2q3oUVmwBbyij1weylsZ8XpBPb17vO57J8sTR2abUd4ARRUTxfIVNoGMg2UkN5Vz+M2vSM3bcTWN3TRNgVwZD1cnwNsUrcwyQu0XYR+Seukig0wyIR1eIzww1TlFWJZNG5k2PQuZZYT2HlXa4Nv0TNTPxH2pLf1zpC6/Q7iP12zFx2u2dqRlWMYVPU1TutispnG8TXNdXCIqxL42rV/s1ru0CxN58laL7z4jLtM01io0+4zQTdO4VWPQ0zSO8YvSE2xLFF+urPAe+U49TUMZTnQ7+KL0fNr0TEQnkzJNwzHqOW1RILJ6Mof9BJqcZ8C2DF1KltxoZcQVd2XDK4YTPA5iphgSW02QhhGqPSTkieKJeZbGh6W9DsOJ3bLXwjZDY3L3f8trNpzmqyWP10rgbRmSDSNMUG16lju1V3LerLjt8izjQ4en//U6eNIzX6HYhenQjkN0fWvQz7qQslZGxMcbydM0IWoZ9I5S9BahsEPjiyETZ8tIsaNafpqGEOrt4L3msVUgO1W6L081ZbF7PrSnJufg2ceB2meE5WwaipNhc0quOj2W44POxULIC4+iHvPYy9/1XDGPtG2fo6TpbRofP0lZClPWyognHD0Jjed30HNzIi80S+PO/uaxAvlXP4Tib0CNpcR5aa/71xvVNE3It4MH/FFkO9OTl7Avq2lk+4zQ7DOSs4wUZK5CPWfZgVV63uBdTWMfhzoll4B+WGHtsFpgg/6ILHNlxMETMP9UGEdeSpzGCdfNfhRpsE4dA8sGTcVxKfPmTF8VGZ99RliW9uZ+E9CZ3IObpuFvN9nYFhO9Y3zDM4wodtNoBAJtQ1DQy44YUyQLLWkKp+f80l6GdFVgnRw3fxOKS8ezz4incu92z0NmFsMI89Jer3CS61aEMldGWGA3MfIuj1UPvwAsjZs3p+AcWAv+drknLT9Xn5Giq/m/aKZpohF3R+LAT2aljC96Ng1LGFqYpmlMf9MranZh+1QnLYJQi8G4mqbAMiKx3dM6sKocFA2DbzVN3GGahn71l5tphFkcKVglCnpYKm9lhGVpr30AhrCFQXmUFDXmtCAbIF051EroNLD4YTp1Gh/sfEZy/SchdAOL56ZnNAJyYE13TL8ah3D0GzjR5KMCR58RzvREjVWpeNQsC0NcO6dnqzih2fSs8G/DrFTLEI3HgVV02tN1aa9teLFpIXqfEUUmdw7KWxlhwdP1n37+O2gNVORLssjT3XrfMsjwdGxB1Q/NIC8TJ4Un+xFmqeeCnzTz/5GIrXnFNj2ZWH2m6qxf86zpUYXxnt6UWlyWZsLxLtgFS8QiRcqMKp8RVTN8+akDgYFbxtJxHsuIk/+qjKnm4HxG9Km9JQDtjJtNTKqBnl3rVabABtj+QnFqb0EGpk3PLP2Diu7C+TACu9U0nRfolvYKCOYHDroStYneh3brVMv8LiMs1lPz72RM7IE6KdqFA2F+mqZATj9Ory7GbAmR3ffxOLB6PTsGGzplfk43JEwLOeQRtEUs7F2WUtg0Utuus+BP+icZ8i0gALCbA83X5JpVVWLXGQP+dMKOS3sNu+3gO+KAzmckYrh3R35M0xgc+fB0kO5f0nJLSihPTc7nX1Ai2vfeTuaYTWTZm56pnqah1jMVP0+PVboO+fLn52kZ4U/aM09PJSo8szTlrYx4wjPFQPFwg3ZsFcnGM66EMgTl1W1a2mu1jNgMQCpXrLgNylQ+I55z3GpkZ1raaxO4+BL7F559XlQiUcG/mobBMlIUtdhaxnRQnqNvVOHfxdvBq5lBoLcyyHCIN8U3+N5bx748N+h7Lld2vm9Xx750ge4zub5T3sqI6IlXHj4jLEl5xjHFl9eEvF4yz/gCslB99XKnzo5pO3iK8Mrm1m0GnpwCm930jHI7eLcvTBEBXShamuuQkfCg4kPLsK1mwm81o7Y2Ul5jkcNOgbU+m/xBeaoUVYbVROZ4zvd44JmmEcnYq9wsz5HZYu0RQfaJyCKUtzLiCcNMYMeTNGyuFcVy7KD9aQ0qFQi7Lzq/2GNwrVD8jMOUDWCvnPB45dOQnd5w7pGppmlCsOmZ59ei7TU6ZYY2TGefLK/AvN8wbJueWeuheKk2k2XEptGsbWzBi5+sz//OBSmUU+UUguN9k8+I3IZqGAbXPiOelhHPfJ3v2VpGfBgLihYbaAfW4HDUSG1bjkRrBI8DlSI/DGdZKL9iRPKmCcNQV72q2FduOE3N0HT0qqZpDMP565B2APL++vNJ8XW67qSQU1vkuMRhwnHTM870ZPsdsPmM2IeefdebRWGCNt1bV+OZ+wBx6bgcWCU/u0KYPBep5TAK/t8rFGvaaihrZcQTrzWqHi+J07N13IHVp8YgpkB4z42KOkXx1oNo9ZlX01gsIzY9hlLLiFUZ6bhAs/sqQLPpGa907lifPaviXWRZ44pVLI/U8rJYRhz+do1jONSD9SLLpmc0Dqz57eDp02VBhhVBhmwy31vaU6FdbwfkM2JYOpqglVCtjMiGxqzM8dhVabCiX6gq8jaFUS9GHpPPSEEHcfeiLzHx6ueKwquzjBQrErmsaJb1Auq3g5+2Sx+h+Iag95wf7cKpqnl9RnimB/JxBV9IqlN7bSwjQe2BUQiPQue2FJrn+YlagF2naVT6jLCkF7BpJBZo7kHj+aJ5vQYelhOnVAO2jEjFzmBkqhcfRRHMq7A1FPbd/122vigsoG7Aj7hM09BuzBaNqHVg7V2dsk/X4hDHmk+xRYg9jukepbmaFkIIo88Iz4dHccXZT9PI3fQsd36NSosfXThxi15FIoqWdooDeSjxfo78dWbvMyIHr9OERa3YMtGWERFspmloLBhBKx0iFgkhcyQlvI5Uog5YrDuwqjxq3XGahnIk9BpQRNsgzSoZN1GdFRXD8stbUL9fpyB8RgCbWRqmaRrvwHkriFF4jT4PWljG9eIdn0Un7vhwTI9KWTboAkqEJ7egx6UyV0acNGebiWYF84yO+drdMWmwMmURjV+cAr2sFAMNg4A8j6iwszX5jFAMOcq+II3iemO1jHjsBi/chmj9nlirKEwOrHYQwj91Qd1ebJ6dXVwWKWhEziks6to1pyKhphtmwvMjTEAu20fj+H5R1mFuCPMIo3LlEitlroyIUvyW0Kx6CVoDpduGndM6IaFs3A6swtM0hUt7vcP76cCae2TUlhHFS3sdN+4r7Nw8O3A5yqirKdrwDsMK7+oHYcXfUgYWpYgmaDq/tJdFKnZonOA7w1rvUeZhGLh15h44Yly/onu1FQm6RArTo7LjOcV1lztIvxxF39tclLUy4t0GiqdhZOA0iFF7kUtsNKKdtO0iI4/7NPdYwnTmK1YWU3ugOv9FnWXEmnSubGmGaXCaQZoX+hVhbG2dVix/Nj1zWNrLOXawWfksljGbuEyWEYrQ+R1YA/5CdtDDO/6ml+3Q3fphwQ92K7revTKOg8f0FRNKInZPRkT5KYzv5U8VsP5hoqyVEWE8NATRY6BVITIQ+eMz4nfELHe9tiL/N93GYmL5OZHtIuwHI9ppGq+qEO2GVLVtr1OhWfMM/F0r+JvWkmYXys7HhkUpYpmmUf2FzDLlwW0lzce3T2Dvneq40iu6nrOIe0R2ux/kgiXtwBp2Om27xdfMAYvu0zxQR8uIm0imuT15OL5kitOnzSMo3Z3mS5JrW2kKDKO4ueV+0k7TBD2geIWzO5k4e11u/iLYVbVfA0fx8xe0+lGEyWSK8w5ioCyemgl+xBR3+GZLQHTxg90QZheGZVpVNeWtjASlkgatgkrE6SsuCGRmK2PLdV7svoLzlhGmE2N5b3rj+JXvoETxIuxXQtEps8C6R4X5e4bSMmITTLipUfmMFJ9Nw9LevGDx93CKw1oNzhYN1nTcI7hPh4QYjvapivJWRjxxn4YxXyqeo2N9EYLWTAtx+6J1j2c/vSAjb+948iqQajt4hasOrGXJDf70y489nhOPYBTxC9sHz9JehgUnzHFkIDI2iygUdocnssDmM9KJTGUkh8hUr4hCJ4K4jxNjfozheeI7vYNBUd7KSECWEa5VGBLmUW2TFTY/2lyjbOJUHQuDfDJfrCVfbvIMo3I1TZEDa8fvFRu3syXkdEtQdierUNGXLOsXaIh6R9tDe5n7DPbpVTvHQj+qJVe0wnbNuPWOK9TP1vRBZ64L5kFdUInwSqczPecAck9ZZ+tbmVYu6WmaEMPlM+L9cGm+KlVCpwPQDTbF8cQJcllxjjdXfOsZRt00jfhwpPo5yejk7Z4z16m9bgOBjcVShMLB+dR9hqFHZZw6ruhp2SL9A+9Jw0osI55WO7eBnT4XmfD2h6zhOsP7MRZY3rWA7SRlrYwQ4rHpmSJ4vqjNXwcy5RNLS2TTMypTIpMs/qJqPwYDEiwM8DJ3MwpVlL5T5+xTB+dDZ203DhcOzj89aCSqUvQnarC0l+LpTrHyMu2NUvC3VJ8R6q96898h8F9lWgFUdE+qIIzBXa2jwU132lHWyog3HgZCw/0+qzbtV2MQcQr0jCqhDLxJ+O2AFVPowFp0TXYegvEd9xkp/JsnEx4lzKPDzQaRU4OFlhEaa4XBUSEq+gea6aVcCJMDq7zjXToRsNqx70CqtrOl355ermVTFmFQ9HJoZUQIm2kaZR+Caub2VPiM0N6nMsEHra67oGyaBhIsIx4RhH1GhAdW++65eHLKQaGnyl0Me6fPzmusMohY+VTvimrKW/E0DUv+WQufYfodBN7vk/s9Wd0YbTK07nhh6l61MuKGIrWRZ58RjTf+T9OosowUr6bh+bL3OrFTBNqvdz/myl2/pDnlcIKYLCPeK1x4LEUiTuFOsKgUhe1api5Cv1JK3nslzYFVWJJw5qf3GemSGAX/b7pUHDLghy6SPavzmQxTvfJ4nChb2gvn1TQsaajEKX2WU1VppqNolB4/rWeFlgLWXFmUVzulTqSULKtiCvOh3WSPBa9yuE5tUSs0cvFqhzRO1H7S6bjtIpfFYqMdWAPEcR41/4RMb4W0fB07csovWanTNI6y8MeXuRcAm2nb35dJ1XbwMGyUOtYkvEzDwtNzdJ+cQXdwInjtwEqjINAcnFkUx87/zBeH3eLt4OU6sFKGs07TKBgww6bcF+Un+CHL01yC/kgua2VEKp0qcuclh6CqzPu0iGTP7iPCY3bnE9DvalX1HCM25n/Zg5H42TTe19kOorRPl0ZKV50r58tFkQ4NJgdWpU6JNsqoSCFYdu41TdOo2PTMU4VziUubB708dOnZJ2gU/WEvS9ADvR3ZIxnCI1hZKyMGPFzFzbZgefkKaq1+zKmKJCDyBUiRfGhQdmov6KcrnNNwr13ReqX2e2L8wlO1OoEHe/fVgmkag01JFLEYij4v/qW9Yvma0uUoP+csjWN8XoKYaLG/Svt+uKVin0vQigmXMnLLLbdg6NChSKVSmDJlCt544w3X8DfeeCNGjRqFiooKDBo0COeddx6am5u5BPYXVV+PAVtGqJQAysHG476f5kK/a1WdAyuUF0Y0ecelvYXTEsxWNJvBh6KOXVcyMKRDA6uhwHD42zNeUT2IKWA0cueDFE7TyNRGipNnvi/LCVaaVavYIC4lPz8QVfRkw6yM3H///Zg3bx7mz5+Pt956C+PHj8f06dOxbt062/D33nsvLrzwQsyfPx8fffQR/vrXv+L+++/HxRdfLCy8OuweC13PKtLo3DtV+k6eKU9RhcvuGr1phCJI0K+IM+osI0aRosPTeXo5r4lA/ZXLeJ06viJLoQmbEbxw2oLVqVRoB1bBMjId8FcQVKYDK08JDFgUXNb3QFrb8LcfkuUzwvJBELQVmlkZueGGG3Daaadh9uzZGDNmDG677TZUVlbizjvvtA3/2muvYe+998bxxx+PoUOH4uCDD8Zxxx3naU3xA8/50KCfToCU5ivsLyotIzIsTK55qLL6FYVjHTzkYuPKJURhj0Hz/AuDMO3AavktqvdSWUY8HHb9wo9ul3na03NQd1P8xQ45VEXWAGuYfgcJkzLS2tqKJUuWYNq0aZ0JRCKYNm0aFi1aZBtn6tSpWLJkSV75+OKLL/Dkk0/isMMOc8ynpaUFjY2Npv8Ch9LpSuSLLYizaURfSq/fPPAm4b8Dq5p07awafm56RYOoIua06VmxNuMQ35SWkChMmB1YwdRYheT0cUAr1D/SKqZpvAZ2kxXYYiGUbBkQhWrKm0MOUbHpD8orjhMU9AcrANiwYQPS6TT69u1rut63b198/PHHtnGOP/54bNiwAfvssw8IIWhvb8eZZ57pOk2zYMECXHnllSyi8eGp9vvV0wSevNT8JM7ShPKLIoeqaRq7LoTZwuAxhSA8TUOZLms2YZqW8zq1l8qfxfTlSTm1pUDx4LVwSN2BlVGRsIsWlEMq3aeoeuh9ZmjSCtPb5sNqmhdeeAG//vWv8ac//QlvvfUW/vWvf+GJJ57A1Vdf7RjnoosuwpYtW/L/ffXVV6rFpIB9jly2KVAWgt+0Hnf5vmRE49jlrRql0zSCgzpNHiLQlF3GLChVp+p6T+48DbFYRliSFanziIdy6QWLSlGocKmYpmEZ2IvDsvfDMhDx4eBxPpZlpSglnxEmy0hdXR2i0SjWrl1rur527VrU19fbxrnssstw4okn4tRTTwUA7LbbbmhqasLpp5+OSy65BBGbnaOSySSSySSLaFw4vmi5h8L0dBRbRgr/lthqHL9wqV96m+6CegkfnYkzrKh6ea1zufmLTGl4fV2LTrOIhaO1rIjmL4KtD4VlaS8LIk6/osVlOSivkGA2PXP+oJE13cGeDv10h0qolRpqeQybv4KByTKSSCQwceJELFy4MH8tk8lg4cKFaGhosI2zffv2IoUjGo0CULOhjjI4Wpvf84R+ZMS6XJPrTBVOAf2fylKTYcQwYFjeTNkbbKlaTSO66otresrdNOIZhIXCU2ztzhAqyr5wIGXIx/q8IxR5qUCBywhTOXgthNI3CWRUqq1xwzoWmOQqJZ8RAJg3bx5mzZqFSZMmYfLkybjxxhvR1NSE2bNnAwBOOukkDBgwAAsWLAAAHHnkkbjhhhuw++67Y8qUKfjss89w2WWX4cgjj8wrJcHhtOkZ/9yCsg6Ds1PzTNZpUBH4QpU5rxm4uu6CUgdWm2scKXHcoYO27G5fpnbfIsUHBDrFL/yiU/Mg7JbDMi2RtSAyrSfarbB89xWGlenAyrvpm5jDvT/THX5B37d2jEceablPifkLszIyY8YMrF+/HpdffjnWrFmDCRMm4Omnn847ta5cudJkCbn00kthGAYuvfRSfPPNN+jduzeOPPJIXHPNNfJK4QssM50MqRZ0ymFp8CKoWF0TVlQelEc7KDumodgy4rgDa8ied+eqCjmCWd9VJmsHg4Ivw8JYCJUS5bGviiy8e1L1iqbslY7uxjn2UvjxHkk51FQizMoIAMydOxdz5861vffCCy+YM4jFMH/+fMyfP58nq2ARnJoJl69yJ7TL0WiuqyghtwOr39WtzDJit5qGJx0p4gil7awEyFNm/HzuOUMBT5ZCYnKY+gthsowU/C11O3jacC4B2ZUISThN03Bae1TTqYR7hVOv+NFS1mfTML2hqj814d6wVe3AqgJa0x+dO0B4C6vywEPRL2PZ22EXx5cbjje8Suw3AcudbJublnVPwzzFQDlwQb7Sz6tTSF3aS4lh+aGk72Od7vG679Z38yjYPrwJRdM0Ab985a2MeMJhGSn8m2qw9R+qRsf9JSBBKQsgZphyM4xiRYfVP0W57kyRAM/SXhlOr3b3ZD2r3ODMZRkREMLOj4gFlumWwqBKlBGBtslqXZE1wIr61gXXN3l8lJgs+cFS1sqI8zyq3WNRMwibzWRu4URz8k5XWvzCMlFae5jSDwnK9hmxqxeeKUMJsjhBrRw5KrWMEajjy8P+1F5z/iwWKPqBtHjljGhbYzoorwCpS3s5pvYMSzxpDqmSw9MoxEz5+TAtZK3boClrZcQTlicVpqfqgYik3uZKgcQFE/H7EURUHZRn2E3TMKYheN+LMDmwep0Lkv1XTl6dPiPsCYquphHyGWEK2xk647Tg0IP7T98Lo+urbe+JOIPSIttx2VE5EIwfNGGa/i9vZcRJ6/f42neDeemZV742t/zYb4Bl6+riuJR5hHQaixZVj4GQ4voPm2MnvWHEQWlxCk/79exDJ+rmM4L8YEefHn3Z7K6JWkYoNj2zCcJrGeleGS+6xrNhV1j8GjyzpTVrq5YjH85bCTcMc4JB++eVtzLiCcvDCfOw6R/lVAsqD68TdmD16IREFVpHy4igkiC7So38v3JT5lG4RX1GROBdFaPEZcSjLP44b7K+T2IyhbVfpP0Y9gOtjFDDPklOtd15AJopq1w0153C+Dkg+f0uKV1NY/3NVY8K5aOe/3e+TrPpmaNXl+m98Y+8AyuXpUpUUv74LJu1mTY949RG7I+JoIzr0ieKHFMhgpclzy0/Hklk+fK5G2zMvklBK0zlrYx4HU7jOaoSj/vyCFOjkQVNOYI+1toNVcoIASlKmz0nuZ1jURqUPiNBPz0xPwu7TcA60uVwtBWZvvTHgbU4UNBLe7NTCfKn5JiTEVYO5AiudOo14L62vJURBYh8sfnVFqiUAMfrlkHSOvhIKANvGn6/S2o7BrG8RB0evaCZouJa2uvxmzYdnjA05AbnXPnZzlnhF0LYgdXnaRq3qUCvYrhOMTLkLxNn5dPwzM/q9yKWH238XN7uHyWG5XeQlLcy4vmmeT0q8UfJ9YUVdKvxgHo7Zx8HERUoOxOFFHciUk9qlpAW7Ze68yoEOssK3eJ7RY3E1oG1I0fa8vMZ6SmusMG7rXsQR5la/Y6KLCUy8uBQ7sMAtRgc72fQZSxvZUQmdqtKGE0QfrUFui9JusGi6L6EUnCf2uuzbq/SgdWK0CotCenRxhetEvlz/XLTsyym8fGdFSsJjVKRK5tJcRGYpnG0JXj1Ia5WBlolUC5eZXHLzzLTRJefH9NRRrHiFyRlroyI+ow4xpSOL1+CHHg5qrmbXL3LEZ6SFqNqn5Ecos7NKqUTtQzIVIaULe21vSZwOA0lWedec+7ilhG+MLyqiK0dmeNBGTACc1Y2yRGSjki2MhaWcgFlr4yIUviq8n7NF/wdoobBK0qYyqAaVUUleb8E/q8W77liMemd9DBDQGbeODTpST+115K+V/6iiMpP44hqq3wJzNOwTtF13lcPu6XRQ2YPa47MpfmysEql9xkJNYbD3y4xSmDVi8xG5+WEyDVjzime/w6sii0jDn/TxlUpH4+vk/W60EAH9e+ZnZ9F59Je1VYxiy3UEMuTbQfWgr95l/Z69Au0kYvSoUxE9vNRNS2pik4l3CtgcZygKGtlxPFFo39rCv7ktIxQmuL9djQKSx5BvyBuRBW/PTKfuez242gZccmXBlXPW1ayRWfTeFlGOPKwizNj0iCOlDqhm6axWcoskCf3R4XLb1kfUn76JmU/DOSkJ1PqrHNweDrYslZGPGHqwY2C/89FCc+DNiEgVtGSU4+kuUz17FGE4vGibDWNTfqiZl4Zz4U1vtsAaPUFEJPFvyefUe8yUsTDZ0/FtDF9xRxYOU/t5bdeFUtLrcC5Tnnw5i6Gs4UvnH08zXbw1vtBl6W8lRGvTc98xr0thH/6J4fcDiO8pVW+mkaqZYRfsXFIUXK4XGj26R910zTOF/MH8An4EtDGGdGnij0RCyyraVjjOSFrVZyKqW9Vvkmy8nKb3hSVJx/G47fflLcy4onX43HfgZXm4QZhJqNrqA5mQo/IMvbH4PcZ8bkuFeWXGxRElJ3ibbSt98VwdmAVS1edA6uc9IKwjOQQKQOTZaSgX+P1GXFD5Gudug58ekDU4gQ00ntN/RuW30FS5sqI/YvGco6DFZE5creofvuM0GLvqEb7desdLkxltaLaMmLeA4B9mqYwSoS2oVEiuumZkwxhWIGTw21pr2wlpxAvRZIHmoPyRPo9K7b9AqfF1DRgBvb9zm8Bk7ssX649Wb7FlJ8yV0Y88NQAQqohyEDWfL6cZEJJVJVlxDLgARLq0ZKAKgtG0PPOVmjnzmnJZDpTppUgDNBYOGynaRQs7WUZ2MNRe+IE9l54KElh+sjVyogNIhoi6452piA+tQaRXIq/WmxNI3RplciA5oSyL3KbqYAwWQwABsuIy3XbU3s5rGoqt+W3Yj2114+WSuuf4gaVz0ju30IHVk5rif2nm7jFNHwOrDSR5eUnO77h8HcQlLUy4vy1YFj+tf7tP36bKqUNZKIvlRwplODnXhOip7YWTinJkFrVsmxlPiOSW5JKXwElzcrng/IAee1MhaVE/rvrYe2RlYtEsa11GzRlrYx44vmkit9UcxQan4jCLzx/ED09lOW3n/idt6iC4ETn0t5OeHyR3JYGi3bG1NtSM1q/eCY//HzuVsuIH5nLyIpqB9aOMKZNzzjzs3u+tH42rvep250/yqe6Iy3kvJ8s0gWtmJS5MsKw6VnAT0p0m23m/HyIS2XhDPoNcUG5A6tA+m6rEGRUKW3Zw7gNNi12UxSdU2hi01SucRTUAcs0jelaAMf2WnfXVbHbrl/TOLT3qfORk0w+LdaPZ5WUuTLihdfD8TDN0ZiyGcPLQEzR8DJHGq6/qfLgFND/U3v9m6YRXSJtjS0qOU1dew1kfirV8pb25s4NMqevEhmyUykV+VN7Cy9JXGFj+dcxnEuAUvQZsVopRfJjxXMrBgV58lLWyojh8IbaXw7YMlL4tx+duNBUjpgJ3xSGWwr1KHdgFZgrt4Y3HbonoVapLSNOnbhzDNMvJ78uP1YBuG0CRt/GOZRIBa2eZprGDl7LiCzLqpEdyQvuhbNH8EsqamWMzuxs/skujlTKWhnxxOf5kLC+aCyEyYdENb4elCduynD/zZqcorLLd2ClnTunxDI4+9NFiGfCNk2jdm6GZUqjqNlKsxgwhhd4BjxHH/g1HSVqfZWJVkZssH0otg/K3YFV1aMV7ZxELBLFykZxSHrp7EOyLo/mCSsDdT4jxSfD8ph5VVaHqLmc1mJC4+jqpxJf5MCqAoEpAUd4D8qTeGovtfNp0VSv/d/M+StExtSSTGgda3nqVhVlrYwUvmgB+GkBoP/6LVULQ4mKTYXqspmW4wp+yZmmaTjmsK3Q+Mv45gPl9t5Izqvo1F6P8GFp/1SraeyuBeHA6sMUnN9WaOZpVkclXK7viR91TUtZKyNs2Kr6Nlf4vur9hEprpv1ytfktWm5epczv6o4oMo10DgCFlhF2VK7Aop+7drZs2G56FtDSTVrylhEfW1un4sOfJ4tOYXZg5cNOVsPlHnW61BY5uc/Hy4HV6wwYv+FxrA16vCpzZcTJMpJrYeqfzoTBPay52mJq7CFVcuwQtfaE2Y9G/Woa/ryKptMc/uaFTzliT5dmmkCdA6vL0t58F+GeOY9sKorDux08r+OrCOY6tW5ZLqd2fB94mS2bDtcD+qjwg1jQApQMkr3i/zNvP7y+fBN6VCbw0ifrRSRjRqgBWgc5j98Ssii657hvbojmiUWw2w6eZ4QyTwFa5uGFrVfeCVjHMeuzo3bNEkB2erbPxidEykLnwNqx6VmhZUSiLtKpwHmEk5iXLByVg4477hZbI7BpIVeLTZFvTrCaSXlbRhx8RjrX1ns9HK8dWJ3ZqU81Zk4ZgmiBqZ/WiuBHk5FlSnV/GdjSyv4Ojyrvp2WEOa7lt9n/RFxunqW9Mj33aduYCPY+FGbnYq+c+fbZkV8eGqXCbSkzKzJXvYRpKsEvvKaFeOO7htPTNKVCmbwFjFhrxc/lrkGjWpbCTphZ8THgaloRlZ1v/wyaMLLn+uWmR/uZIhO/B2CZG50VYlj+dQznYWWQKQt1eK/pONe4wSlRXh+4Kna35UUrIzbkGx5Ph2vY/+0Y3i5fzzzUNxtnB1bvvGm/ZBydGzmL57fVRN3ZNOZdPgEZyoNgAhZ4toOnse7xLBlWPV1WSMYyT8OSd1UyShVOtG54sbWeBOEzYv3N2Kf6id+rfeiPIQiHRY6FslZGmJb2hu0t0ASOqtU0OURWw3jt1SDanHmW9qqaTvHzzWT1GSmsgz0G98CJew3BGfsNZ8ozyLn8ILY8cBsUZdWEfN8kfsuJSlwtNvB/+t+NslZGvPGeES6+wjaABKGNCvkiePhwyBjoeJfJ+V2TqvKzX/LKno4f1gNPGZzmpA3Bj+7Alvbmss/5jNDLYRgGrj56LH6yzzCuvFUrJflTeyU4sPJYRGni0ufvT/tQZhkR7UOpxp5wWZ3KXBnxWNrrec0/wqTBsuClmXvGD3FhI4reHuvGWgD7QORqlZBQp7RTVE66iGN4HqVL1TSNvQtrNk+BdEV8Jvwm+KW9qgZMsfeJLa4hTzmSbdEx+YzoaZrwIurxT+VfIS07aqRuelZ0gUskqryzyYfHNOLnahou/1WXaR7hHVgpeo6ipb1FVrXiOEHPW3vRaRnpuMAjLuuz5PBP4YFY/rX+7RfuHzDsSrBKgh7APfGwUGnLSFhwOrXX7hW0fVL8S3tZKdnlbW7zvzSmROvbFKKyqxo4c83SvIW7oPJQmJZQSrk0KAcFB+uOs4Mebf48sdhwc2DN7y/hkYatwuURK6gmbru0l3uaRl4pwtD3eT4zj28kWWJTvx+KLJcqKW9lxBP1j8eP/RJ44P0CUV2G8NSQyoPysoh0FIbh/iSc+qq9hvfEb384zjN9nrKXlBLtgHUHVj/wKyu7Tc9U4FV3hfdVyeJ3W2S2bEqb1eFXovxGKyM2GIZdtdD5kRjut7mR6TMiOv/JEkbU5Moiq/+7HKqeppH4dWkyxzqnW5WM4Zg9B6Eq6b45M88ydJopSeqlvT6Yl+3GwYx10zMFShnNScV+wbvniIisRSvBKNuuUxyV5KfQOJ3u2fOTPU0lcWARpMyVEY+lvT606DDN2RUSJlnCiirLCLFxkmQ+mwbubctzesHrPtcgTKF8cvSIvjZV5qW9Hh8snGmoQOY0jQh+rJhjfwai+TG+v5KeufuqJstv7cBaInBo5GEdz8W+WqwXir9iaL6AXfPg9DPx3/Tq3zyNqJmXXpkxTP84Ibqaxik2tWUkoLer0zLCLwfvnhT+ObAWfKRxaiPcTujwsKZyWM5UQpNNEC01TBZGWspbGSnc9Mz2QXg9HfHPBtrteHlMlSL40S5p18KbfodIxVPmM2Lz9S08NVf4N029C963zUdCvvb5qHkQbme10CtNdNdY01BBWVlGOCyN7ukJJkAZXGbbsR7fF3TPWt7KCBPyv4CCQkQuL3N/0fQA16oLPvyubdVLe82radjiep4U6uSXkPvi9/p6p5CHELhOSQcx0IlStJrG10anOjNi+sfypzQ8fWYklNOvjxa6Dyr/xwGeacSgx6syV0YKfUaM4qtc6i6/Ckxr2gyniqMGN2e2oPFz0zPRp866tNernumnadyVHpsIdOn6Mh1aPBSzrqaxXdrL2K0E2ea5l/aKPBVLnxiGAdNbgRKLTxuevt15B5QxpS6TMldGGAj6SfkMr9e612/HdOiChUoRU3Z0veXrG+BofoYljodFy3pdRcmsZ/nYDtRcFkheidixntrL5RMVqlbcCSk2jCg5wVdkRkPeklf/MHzOz5Q3g4Uz6FaplREb7DsLNdMN5vHCOYbbwMKKSHTWU3tdw5m+gOwHXpbOx38HVv/SZ9ZFXJQP1uXZdtAeEuhUBmaLiTUcXTAh7H0o2EwjtuXknKbwy4HVdE3B2TTecS1Kq8PfqvK3T89dfZeen1MboI1PM31kySfo7+3yVkZYTu21T0CWJKGD3lLhoXlLzi9MKHOcVJCX23kftuE90uPb9Iw9Es0b5qelwXpqrx+WHP+W9oajP7OW1iSVD0teZWOdavITz/c8RB1veSsjLPAsZaTRTgWcFHkRyofiq45vOS9dFu7Hi/v7ZinfgZXRz8MU1/KbVtZOB1av9DneB5t83MK7phtQJ2pd2qsC0boRpVAp4V7a63ZPYIAM6uPG0S6Sf1/k5iiaHJVVvihQsJpJWSsjxMkyQt0S/Ht4tNM50vLzoWg8Z5WESJFHVHElsSq27mmxKjYe0zRUirb7b/s46i0NtLgu7aXM294vJpw4n1HsL67KiKTK83sVlKzsZC9JNqfNJotsyloZUQ2VdqpcCps8ZS7ttfnNNYgWWkY45fP9ZVI8CPL6zmTDm48tL/YhcVL+6ObA+RyczXHsBvuwDtQ5Op+NyDvE277V1o6tA6sCbcR70zcJ1k+fOgPD8q/sdPkT8E7BasUO+t0rc2XE4dRegTdQVYchMjDx5RcO60tRmKDfmAJU7zPi5OBLFZcyXZ74tGlk03FQeqjjs6UrE7eVJCK2U8+6pUxbNnb9HvdqGomFkGkh7EyTVbsXyMuQKTdbOJaPCr3PSKlg+6Ak7MAqwSIQJF66guyDncJUQ6qUEVsHVo50zJ24OQVHvwTKjGjKbh3baKZtrNeoHFgDaBRiq0XkyaGCwuemxDLidT/k9WNL6EwjFFlYrdjqs3RFKyM22J/aSxnXlI7Mx8s6568GqqknDkuqs+WHvrR+14sqB9a9hvcEYPHzYLWMFE3LOP+iiV90n0MOGhnozfCUAgjgNhB3TmdJqqjCKE6KIntSTMj0GRGxXBXWaZFCS52/XJwtfDTTIRIteZJnqfy2uLvBNerecsstGDp0KFKpFKZMmYI33njDNfzmzZsxZ84c9OvXD8lkEjvvvDOefPJJLoGlwrS0V9H0S0iUDF4CM5uGANmWrLMOGIEbZ0zAsXsO7ki/IC+O9Nw6Gu+pAo95fYmDrCVjjnSDMI2IRA1pW/fJZ8QLa+0UiiDtUUt+BLLfF4kur9T5BN0uY6wR7r//fsybNw+33XYbpkyZghtvvBHTp0/HsmXL0KdPn6Lwra2t+O53v4s+ffrgoYcewoABA/Dll1+itrZWhvyhI0wOQSrw/hLMfgewp1vwN2/+Pg9KsrMb1KMSR+8+oDN9gbysHQv19u3SO+nCvwstPfLSDQLD8i9TXE9jitNXOEdmDDh40HGl5SorTRfiGDWgdixgrQpGV6axfhqhsowwKyM33HADTjvtNMyePRsAcNttt+GJJ57AnXfeiQsvvLAo/J133olNmzbhtddeQzweBwAMHTpUTGppMFhGbJ+U+NOjbQxhajQs0HYsTsF4lof6hWoH1sLCsn61WJ3mWNsP74DpBs9yYBpUPQW3PkH0bJ5SIRjLiGunISkPufin/IhN1xanFx6YlJHW1lYsWbIEF110Uf5aJBLBtGnTsGjRIts4jz32GBoaGjBnzhw8+uij6N27N44//nhccMEFiEajtnFaWlrQ0tKS/93Y2Mgipo94vKlhetKSsBap6CA78JrwxbUtv6tbts+I21SKbEuCcGdHPXlfaA0xK5+luLQ3R64oyqar7OIprp3cahrzpme8aWXQM2VgQHVnH98rZaC5uRndosR03UoMbfn7tQmgLYP87wTaXePmqOvIC4ApfO5aJNNGlU6ObtGMbfh0Wwuam6OodLifLYOBluZmpvzaWlrQHCuu/Jq4e93lSBjtaG5uBmlvdQyfMtJoa2vJ38+0t+brh4V4PO44lrPApIxs2LAB6XQaffv2NV3v27cvPv74Y9s4X3zxBZ5//nnMnDkTTz75JD777DOcffbZaGtrw/z5823jLFiwAFdeeSWLaHw4+IzYv39qvoQMh7/dw5VKl+0uK88SU+vv3/5oHH750LvMcslAuWWkAOGcLIqAH1i99eni0Jrh5U35OOHqwEqrs/morIhi78DKro30rIhg3VcrcPJulWgdU5G/noxFsHz5ckyua8cuBxZP6efoRRpxRcf9ingEBMARw7K/e8a35e+5kcsLgCl87lp1W5oqnRzVqXaMsgm/feNqLN8cwZ692jHaIb2KeBTffPUlU34b13yNzTZfO98dZGBqX+90eiSasHz5csTaM4751lbswLdrvum8v3Udlm/fQC2jKa3aWtTX1wv5bzFP07CSyWTQp08f3H777YhGo5g4cSK++eYb/O53v3NURi666CLMmzcv/7uxsRGDBg1SLaqGB5tpFK55dNpw1iWqHHnJwsdZGq6CFiqC1n7NsdOQOMh6fVWXsvNzfjWNQNywIWPTMwPAcWOrEY/HUD9gIJrbOxNIxaMY0qsb1m1txrdNrY5p9O+eQnRL9gu9KhkDAdDU0g4AqK9JIdbo/fWeywsAWis6LevD6msAAE0tbYh8u4O6XD27JbDJRuYhdd2QiEWxfmuz7f1cGfrVVqBt7Vbq/Ib2rkIsWry+JLV5Bxqb2zzj961JobYygR2t7cCm7bZh+lSnUJWKIb1+GwBgcM9uSCXYLByEEGzfvh3r1q0DAPTr148pfiFMykhdXR2i0SjWrl1rur527VrU19fbxunXr1+RGWeXXXbBmjVr0NraikQiURQnmUwimUyyiCYV246awwGQqtMxzU64WRHUfwnSwOxISTv9a9hf9z4CO7h6kT2ouFmBWK0wKqd8rOnRykEzE0edLmU4EbJWAfucqC0jHHFF94CRCatdpDoZwW59U6ir641NbVEYSOfvReMxpFIpxFsIjBbnNBKpFIymTEecePYppLMDcyKZghHLeMqRywsAjFin8pK71oYojFjaNq4d8UTSVuZkKoVkLOpaplgijlQqBSPmUmgLqVTKVhmJJTIw2r0bQiKZRCqVRMZohxFrtw0TTyaRSsVhxFrzZWFVRgCgoiJr/Vq3bh369OnDPWXDtLQ3kUhg4sSJWLhwYf5aJpPBwoUL0dDQYBtn7733xmeffYZMprMBffLJJ+jXr5+tIuIvBdM0XC96OL9w/ERWDfCmE+QTiCjepUfsoDy+AwtVIsNxVlYcWXCdpaNADhkQm7W9rJaRbnED0UgEiY7FCnyEsYbCKBMFLA6sAkWsrKwEALS1eVttnGDuTufNm4e//OUv+Pvf/46PPvoIZ511FpqamvKra0466SSTg+tZZ52FTZs24ZxzzsEnn3yCJ554Ar/+9a8xZ84cbqGDwe5Jub+pdD4R4bB40OK5nh4GXwdNaeFwc8T0/9ReyZYRV2uGWF4Rk2LjYoGzyds2HMdqErqDEdktkMpQtJLEu+6C6QjsDwZkq4T8dKCblZcpRb7IvvlF+Z4jLf7KI2OvH2afkRkzZmD9+vW4/PLLsWbNGkyYMAFPP/103ql15cqViBR8Mg4aNAjPPPMMzjvvPIwbNw4DBgzAOeecgwsuuEBYeGGYNj0LFtlmdll4H3rlFtf+b7f4YVrqqzprkQVGVt8dlfuHUMdRZRlhjyJMrt3LUtrCgG0fGPaOsUQI2oJcCnA5sM6dOxdz5861vffCCy8UXWtoaMD//vc/nqzCgyJzbKltkkYz383n1Ff4N19N+O4zItsyQmGxkJW2s1+COmuPTOUo6DFeJHtvBYbtumwIy/5LCnD9gPFNCvUc2jAOM085CyecehYAYPygHvj9X/6J7xxyeMCSBUOZn01j2mjY9iorqjoMc7ql80q614d3OYrjWwbVAOsiqupwmg5EpvAMayQBK4tt+hxWDmVWAkXJuvUDtPuM2N0OWpFyxG6aJohdzzTBEPCjLnNlhAXbbkVuqqHtpZwpmkYBrxMin69Al5qmsatMztys9Vm0tNcHJY4Qq8Wr+L4Vvmma0npvwjp1k7OImE7tVZJTOMuvilCXNkTCaWXEBvrOovhVVbXUtNQcXXPQbnrmZMJnGWj8rhbl+4xIzEv2gYxcjqYUU5LU6fqiTDkPxbS1KdMHRrUSY+vAWiaGkVN+fAQWXPZL/PaKi7DP2KE4cPed8f/u/Tu2b2/CZfPmYKeBvXHEPnvglf8+l4/z2bKPcPT3jkJNTQ12GtgbJ//gUHy1Ynk+vd9ecZEpj3NPmYnLzjtbTFCpY4q6tHkob2WExYFVUUcgsnwzDEjbuIoynWKH1uBqTfk+IwL+RAbclZkgfDbcyucIxWAYRBMIq3VDBjItI4QQNLel0dyWxo62NLa3tmNHazp/ze6/7a3tnXFa06bwXnEL86JTJjv590P3obZHL9zz74U47uTTcc3FP8f5Z56MCZMm49mXFqFhvwNxyTlnYseO7Vi7ehV+8qPDkUwm8fzzz+OZF17D0TNOQDptt6eHRI1UMmFqxcp3YC03ZDhihhkqB1Yu/wQ+pUym9YAV9ZaRzgxElxGbnwmNr46cwpkthfZ/m8PTpiskFhU0A7G3zwi7oKIbwvFiux08r2mkQ9iW9gyO+XMwixc+vGo6KhP0Q9zOu+yK08/5BQDglLnn4c4/3Yjanr3ww+NnoU9NCmec+0s8cPed+PSjD/DCs0+hqqYG/7zn/9CtIok1jc2o7hueXcJLceTRlpHcn6YbavxD7KAduMO66kZkUKBRJLy+6IPd8Ep2eoblN39eRUphkeOv/9DkyWM6DmtZSg07xaNMZmkAZJWRHNFoFLU9emDk6DEAss+7V+/sGS6bNqzHsg/fwx6TG/In0XvBqtzLal8Unx2SchJHW0aEkD9fHHY8Nz0zDL6vQVMazNHzefuJ6vxENnRzU2xkwOekTBPGEohqmsb/Fy2/mkZF2g6pqi4myf8rb54mGYvggTP2AgBUJGIY0bsb1m5pwfptzufLDOlViS83Zs9TqU5mt4Pf1pLd2XNQj0p89a39WSuFVCZjqIizbUsesygWhmEgFoubfgNAJkOQTFWYw1rSMiKRIuWuvZ1/d9JyoKyVEaeTem3fP9uewM6BlU0GnmmdMCk5IlNRfFtpW7/wg/QZ8TGv8E47O+dvtWIFLhEb7jMUnE5OJUaGc5omV2zDMJDqUAoq4lFUJmKoSLTnr9lRmYh1xklEQQC0dxwnUpmIusbNURGPKlVSd95lVzz20P+hra0NiVjxOWo9etZhw7qOM9wMIJ1O47NlH2HPhn2VyVTqlPc0jUYNjP4J2XCUSXflaZqi3/IysPqcyFyd4wQh7lNNtkt7uWQJjjB9GIiSP7VX9dyMSJ2FpL6PPfk0NG3dihNmHofFixfji88/w7//331Y8fmnAIDJe++LlxY+i5cWPoPPPlmGs846C1sbt/gnYAnqyloZscF+AKUcVBmX4PL5jARoDZCYtZNViKUOg3yZZJ9NY0XEZyQbx74e/dr0DGCfcqBXStU/eZpNz7yQOZ0VxHsfvqW94Rg+a3v0xF/ufxTbtm3D/vvvj4P3n4p/3fuP/LTO0TNOwFE/OhaXnnsWZhw1HcOHD2e0igRQzoCfdVlP04B0niRsmqYJ3xsYWuwsFWYlgy8dx3Cc8VQgO2tXZZRlvxWboCqtLrTQ+LEUKRmMCr1f5KchQjI4yoIQYpmy7qp9ofm5/fXBx4tCPLXo3aJr73z1bf7vnXcZiyeefBqJWARrG5uxtrHTDyYej+OSX1+PS359PWorExjcsxKHnXCma/qFaZcj2jJCC6W1xByMbf1AKXRrqmRkrjabwL4PSorz491Az7alMqYV8egZeJRMHv8op7HQj0ftuk+FQjO4U5wglC7+7zJnYf0ohl9VxdbD+wfVqFL8hRIoZa6M2C/tpf8a8O+rQdXOrkFCs/dF8de0XN8HEaRveuZiFBB1YGW1KHlNQXE5Hyt6Vl1l2jIMEGJWQDLlYRgpGaSLHaJ6KHNlhAU1na/suXzVeA1CBgxhxYm3GvwelPx8XmzTNF4WO5r85OCkUAk70PLMBUpEZVtzVMyV5dhJ2HcaCV0XGTqB6Amb6GWtjBj2h/YK+Yz4MY0RpkYk4khI06F7hZBdF7sN6E4dVrrPiHXZsuDg7TRlSFPvbpaRJ3+2L+f0Q5harjdSTu0thS+MAqx9n3af0/hFWSsjTCjqVLjm0UOMcgdW61RGgJ298tU0Dn+zxMthPbXXMw2X8GP613A9L2XTNKX/2oQGUvD/muAIk8+LX5S1MkKQ8Q7EioPDnnCyFP4VfqCuTIV/00/ziPpViKB6V1Pe59y5O6h9PVI5sEooXFYxtXelE03dF8XdZUy2q2PbcBLFKZfVnmEaIHP4baGVllMYK9OBslZGCvF+6RRZRpyWG5QIdg7ZPIoT7+6zXcmBtTj9gr9FfSyKpoDcE5ThwFpk4rfEkTUFEEQTKCUrJsu26FYH1q66zUHpPD2VhKsWtDIiGdZNz0oNP0ztTFMSQepyijPkrWu7gZK1nmQ9Z8elvTIdWBXhuqrOsPzrFEzY16cwSzFLGQ3WMvOqIl2w6ytJSuk5lLcyYjq11yj4226faj98RtzChbNZeTvwUabjeN1g+i0K034evj4SnlGt88+i7eC9oioonCqrVqk5ifoNS+0QYlZAQre0t9wftfSp4fBUaHkrI4oJqwIhgufSXoOv3DL2UfH7vVLtwMrd89hFY5w6o3F4FVkuLNoJyvQ/EclfRd6yT+0VqeuwTdN0lR51/KAeeP7pJwAA33y1EuMH9cDHH7wXsFTBopWRDjy3g6f1fWDtoCkHibDuRyLiwMe6D4ttnEB9RiSn57KhG88OrCIDNtWzYU4zRA2XArdxWLYvlEpKrNo1QCD9WiIarDpQ3mfTaKST3fSMJx5fuCD72VLq5Fl3rpVl9TFZvDji03yXB/Ec8gqfD9NZwukxhM06sKq1hpTQa1OysNTxmP41IASIsq7/l0x5W0YUvHTMX4sOSx/d0g3D11YOz47T1dpj793I4mxpHuz8rRfp/iouv1lystuQizUtGmWEZsrO8R6FDLxpy0JG9yBTTt6kWBSmIgfWQGZp/O/fXvzP09hn1yFIp9MAgI8/eA/jB/XAjQuuyIt0xfk/w0U/Ox0A8Pab/8MpPz4C3aur0KNHD8z4/pFo3LwZAHBowzj8845b82kbACZMmIBbb7hWWE4VNROLRBAP2CoClLsyUkB5nFSpHuumZ9TxqNNnc8RUieoBUepAZp3u8iFvQsz5WBUcmoGORoxgTiDO5R1+WD94zQ6sgn0hITDatuf/Q2sT0NZkumb9D63O993uFYVlkH2PyQ1o2rYNH7+fPUl3yf9eRY+evbB40av5MEv+9yr2bNgHH3/wHk4/7mgMHzkKL73yKl555RUcfOhhSGfSYnXlM2Fru3qaRjKsZtuw+oLYoXT6hdPCEWT9Kd/0jHcpZ0c8R6sbRbJUlhFGueR6nRaUTdFzd/soUdnUnNLm3wSPwTJC3H/TZ9rxT/sO7Pb3XUy3+nT858ZunPdMXLwKSHSjClpd0x2jdt0Nixe9gl3H7443F72CE049C7fd+Ftsb9qG1U0bsXLFF5i419649foFGDNuAi759fUY068GsWgEvQePwJotzbSSqSfkY4kdZW4ZsX/TQmkZCanSYisKpXxUY5PHF73sr+Jd+7OcTaP2QfDuumsXtGhpr+emZ3z5uIYXjC8TFfuohBUWywiBZdOzMPaFipg4ZW+8+b9XQAjB228swkGHHonhO+2Mt9/4H1575WX07tsPQ4aNwLIP38eUvfcPWtwuh7aMKERkZU0YUfo1GBJla+yAGhw6th5Pvb/GM6x8R0M2J1PGxO3+pJZFiggmGejSdxoKg35XqFfT8FgTHeLwF5nFMiLXZ4TEKvDerI8AAJWJGEb07oZ1W5uxtrHFMc6wum5YvqEJAFCTioMA2Nrclr3XqxLLN273zLc6FcfQeCWTrHs27I1HH/gnln34PmLxOIbttDMmNeyDN//3Ctp3bMOkvaYCAJKplGs6RiRirkcDaGtrY5JFvHmHfDCxobwtI6ZNzwqv8ydZ2ARkOn/ROrr6jcimZ3TLR91NIyoGpX1G1lGFU/0ceJ95LizP4J8Pz/NsJIcPGtelvZZ/w4zIOyLWhRmAYYDEK/P/IdENiHczXbP+h4Tzfbd7RWEZC77H5Klo2rYN/7zjT5g4Jat4TNprHyxe9Cpee/klTGrYBwAwcvSueP3VFx3T6dGzDhvWdX7MbG1sxPLlyznqTzEha7zlrYyEAJEBI4wYhsFVDuqzaTx+i2KAXn7ZyzpV7qHCuvsp1dJeHyx/VFYchb5M6hJwSVp2u2IISyDvw8wxslDx1FV8TW0tRu6yK558+MG84jFxylR89P47+PyzTzFpr70BAKfMPQ8fvPM2rrn453j33Xfx8ccf4647bse3mzYCACbvvS8e/9cDeOv11/DpRx/gvDmnIxqlPx+oXClzZUTB0l5F70oYpjHsO0l3YdwGdppzfLyXDheG9bdi/FyWz7ZNvVEUx7C570ZEcc9AWx7HaRrh/L1ToNvjhL/9s8LbvFn2jClyYA3AZ8RVWsXv3KS99kY6ncaeHcpI9x49MGLkKPTpW4+hI0YCAIYO3wm3/fNf+OSj9zG1YS80NDTg6SceRzSa9Xo4Zc55mDhlKn46+1jMPXkGph92BEaMGKFW8C6A9hnpgBQ0cj9fwK52sJ4BteUoXtqr2DrBIItw3h6/edMBbCwjHmmoWE1jTVLeqb3+vzilZMVkaqaWTc9Cthu8NJyq5JdXLMAvr1hguvbAMy+jX/cUVheslpnUsDf+/vAz+dU067c25+9XVdfgt3+6Mx+2Z7cEzjv7dLz79eb8tXe++jb/94BBg02/y5Uyt4zIp5Q6KVZoBjnW+53hDNu/veMV/E0dyyNNSemIIrqhm9nnRL7PEftqGnkyyDjLyAv37eA7/vVIQ+6mZ3yJiYigT+0tTWh8msL2jMpaGTEcHFj9PByKerBWK4ZUaGWlUSS8rAXyfUbCA7csNhGLpl08l/bKrwlWvxWVhOk5q0ZsB9YyM42EJ8GyQ0/TKKQrTLsUQuXTyDgdYBePe3pCQn2Hae8LUVnM8dksCVT7jIgsNhUtm8PffmEU/eERTmqmjNEY4hX7jIQL6v4ksJxlxiwvytoyUvgVIG07+ELfE5rtrikdMHmnMVQjIglNXK8D3sJUF7IxO53yxeOJD9CeTSM/Tb+gE8VlB9bwFMUTJmUElk3PwqaNlAlduV9zQltGNFLJOrCyz+fTT1e5KyeiBOvzIydvu46MdXqrFE6j7Uw3OAfWUvARE5FRhS4S/hrTBEFZW0aUnNqryKkvDC+wXacmNBCYpmbsFZiiQdRlGigMdSQTmYNsoVWCZnCim6bhR3QQF5/m8U5Azqm98p4h/9Je+rBFPiLaNOJOV+t0AqS8lZECSunU3jA7lRmGwbWRG29HK/2j2EBgHQyvv41XOrZpe/k6UPkHsUmoqlq50pXkj8Pb/i44ZDQOGNVbTAhK2BxYnaevNbxojYUGrYxIhrXZhWEzM2rsBjkFSbN9NYfTl0YKgm3DvDSYLS01q2nYp++c07L/WyZuA7HoKrizDhiBu2ZPZpLHe2qN7bodcg0jXex9tCH0JQy9gJ2Uuc9I4ZvW+dTCaHkopXGW3uPde0SRtY8JLWHyGJG5rwTrZnEKdoMvCh/C14wBo+D/ww2rjPJO7S3pByyPUmgkIUBbRhTC2qGH3RmObvULRyRLMN6VI6WksNHAP3UlPlBSWZlY/aNcfIFYMfsYsadGE0PGR4nM6Udeyx/TdvBF+4xwZekhaxd7URHCEnUI9N2DvoPfXnFRsLJQUt7KiNOpvQIUvoTKTu0N0ahL459AE9fRxFy0esb9tyhZn5dg6pd2aTdf2mzhozYRhtV1w/87q0FECiGZhODMzOkdFvUZ8RMmGUl5LO0Vtep5cdl5Z+PcU2YyxuLnzUWvYPygHmjcssV0/f4HH8KcX1zsmxwilLcyEgJKymeEAk7DCHf6XRluB9bcvw6OxFYnY9s0bO6f992dMXFIT275TGnK3PWMBsuo6nf2fiBDJqvu0UV1EU/aWluDFgEA0MopR64t9OrZE92qql3DhIUyV0YULO2VnqLqhBlEELCC2KZH8be13KqX9hqS0pGBzGkN1hOGlTiwSk9RLdaVJXZ4+97IK3UQ9ReE/1wQ5Tzlx0fg15eej99ecRH2HzcCZ57wQ3z68Yc4+8QfYa9RAzFmxGBcfM4Z+HbTxnyc5554FBPGj0dFRQV2HtIfpx93NLZvb8KtN1yLxx76P/z32ScxflAPDOxRiRdeeAEA8OlHH+DUGUdh8k79sN9uw3HVBedie9O2fJo5i8o111yD/v37Y9SoUQCAu+++G5MmTcLOg/rgO3uMwoVzT8XGDesBAN98tRKnHnMkAGDfsUMxflAPnHnaKQCKp2kaN2/GJeeeiX3GDkW/uloceuih+PTTT/P377rrLtTW1uKZZ57BLrvsgqqqKhxyyCFYvXq1moovoMyVkU6CW9pr2PzlThgdbPMY5uGctjPm7bS7gjUphzQHVgql0Stl2zSKwrDJF6ZnFfQ+Jypweh4sZSVEbv9HCEFzekf2v/Yd2N62HTvad3Res/nPdL+94z+7e27/te9g7if//dB9iMfj+PvDT+Oci+bjtGO/h9G7jsP/PfE87nv4MWxcvx7nnzUbALB+7RpcOPdUnDx7Nj766CM88tSzOOiQIwBCMOuMuTj4iO9j7wMOwsIlH+Ptj7/A1KlTsX17E8464Ueo6V6Lex5fiN/ddhf+98oLWHDpL01yvP7qS1i2bBmee+45PP744wCAtrY2XH311Xju5ddx4x3/xKqvV+LyeWcDAOr7D8D1t/8DAPDoi29i4ZKP8ZvrbrAt42XzzsaH7y7FzX+9F88+/xIIITjssMPQ1taWD7N9+3Zcd911uPvuu/HSSy9h5cqV+MUvfsFUlzyU+Woa+YSxk5KF7aZnCr5j2BxY5c5zhen58U/hFTuwOi3zdU7BO5SqDf4KcRpQ/HhMhLj4jOTqWJEgdvl6Tq3JyFeSA2uOlkwzTnnhYLFEOHn9+NdRGa+kDj942HCcd8lVAIDbb7oOo3cdh59deDkAoH9tBa66/g84ePJYrPjiM+xoakJ7ezu+//0fYOjQoaiq64faASPyaaVSKbS1tqCuT1/UVSWRSCTw1CMPoaWlGb+68VZUVnYDAFx09W/xs9nH4dyLr0Cv3n0AABWVlbjjjjuQSCTy6f3kJz8BAKzavAPd6vrjgit/g+OP+A62N21DZbcqdK/tAQDo2as3arp3R/fu3YrK9+Xyz/HCc0/h7w8/jQmTpmBIz0rcc889GDRoEB555BH8+Mc/BpBVfG677TaMGJEtz9y5c3HVVVdR1yMv5a2MENs/Azu1162zCdMg6QXt9ALNAOm1WVcp1YsXKstWbNVwD283rSNzbxAgWOdI2WUJM6z7jMhb2ltajNltQv7vTz56H28uehl7jRoIIFuHuXr5+svlaNjvO5iyz/6YMH4cpk+fjqn7HYg9DzwMNbW1jul/8ekn2HnM2LwiAgATJk1BJpPBis8/zSsjI0ePMSkiALBkyRJcccUVeOvtpdi8eTMymQwAYPU3X2PEzqOpyrf802WIxWLYbfdJ2QsG0KtXL4waNQofffRRPlxlZWVeEQGAfv36Yd26dVR5iFDeyohiSqnDosG2PIzmf8qcfIxlSSOkz4zFAtW50sNeK5S16ZmIA6s1+UPH1mPikB42cRymHnx4UDTDsJ8fEEEs/RdVGJORFP56wLMAgMp4DMN7d8OGba1Y07jDMc6I3lX4fH3Wj6ImGQcBsLUlO40woq4Kn2/Y5hg3R/dUHBWxCiZZKyo7rSjbm7Zh/2mH4NyLrgAA9KlJYV1jMwCgrm9fRKNR/Pneh7FlxQdY+J/ncMefb8XVV87HPx/7DwYOHsKUb5EcFWZrTlNTE6ZPn47p06fjj7f/DZHKGqz+5mucdcIPTdMrsojH46bfhmH48oFe5spIJv8XKXjPRb4GCjsMqlN7XX453QnT0l47aBdN0ITz8qMIeVUwUVwWmc6P1pbmnnbExpvMa5k1swwFP289YSJTWswUbfpGh9Mr3IWanQkCq/+cGIZhIBXNKgWpWAyV8UpUxKJIRZ3jVMQqkIqmO+JklZG29ljRPTdSsbjQXie7jB2P/zz1b/QfNBixWAz9ayuQ2mxWoAzDwN5774399t0HZ8+7AONGj8TzTz+Ok06fg3gigXTGLOfwkTvjsQfvxfbtTXnryNLFryMSiWDoiJGOsnz88cfYuHEjrr32WkSr67BhWws+ePdtU5icApHJONfNsJGj0N7ejvfeXowJk6YAMLBx40YsW7YMY8aMca0PP9AOrBqpqFYOlKff8b8wwL1hluVfa1pU+5mpqGiToUbQgZQ1guQvu876ofuA8AMZj8z6BSz2RezkUCs9SenMmHUqtmz+FhfOPRXvL30Ly7/4HK++sBCXzZuDdDqNd99ejDv+cD0WL16MlStX4vHHHsG3mzZg+MidAQD9Bw7Cpx99gBWff4pNGzegra0Nh33/x0gmU7jsvLPx6ccf4o3XXsa1l12AI34wIz9FY8fgwYORSCTwhz/8AV+uWI4Xnn0St990nSlMvwGDYBgGXvrPM9i0cQO2bctZjzorbMiwETjw4MNw5QXn4q03FuG9d9/BCSecgAEDBuB73/ue9DpkpayVEcNh0zORF5DZqa/Qb8LV5Bv8AEkxS1N8n8OMbR443XNwOu23FCm2XhT8zeLUaxOWdWkvnZOr/DStqDIP07xPhJBQbfrF275ZyiD3bJoQVR4jfer74e8PP410Oo0zT/gB9t9rEn535cWoqemOSCSCqqpqLHl9EY484nDsvPPOWHDVFfj5ZVdjnwO/CwD4wfGzMGT4SBx3+Hew206D8eqrr6KiohK3/vMhbNn8LWYecRB+ccYsTNlnf1z0q9+6ytK7d2/cddddePDBB3HgXnvgzj/diHmXmh1K+/brj7PmXYSbrr0S39l9Z/zivHNs07rq+lswZrfx+NnsY3HwgfuBEIInn3yyaGomCMp8mqY0CfPSXgN8O5jSfilbiy59Xj5ECo1cB1Y25x47nxFRB9swKNQ5ZEmiqkg86WafcXHfQEBMDpieqO5ewtMM8vz1wceLrg0ZNgK//8vdALKraVYVTNMMHzkKt/7zIYzt3x2RiIEN21pM93v2qsOf7/0XAKCuKon+tRV49+vNGLnLrrjj/scc5bj693+yvX7cccfhuOOOw6rNO7BhWwsA4J2vvjWFOePc83HGuedn5avLTgM9t/B5fLymMR+mprYW19x4W7Z8vbqhe4VZCTn55JNx8sknm64dffTRvow5ZW0ZCQOGw99hxG4wYbFc0N5jqQenXUZLkiJ9wXC65ZFMNrTT1AxNPbFaUmjgtfTYpuXTo3byHwtsqss1Q65broT3s8dfSrxnKQm0MmJDGJezldLLwCUrpQNr8f1Sqhk2lC7t9czbRvGkuFKcjv3fpU5e4QtYDhoIoVeMrJuehdkKK0QpPLgyQysjkmE3XRf+He43hMZnREYRWOqhKw12rgoDl8+IfeVIW9pLkU7hWCbTCiishDLKrigLtvQEEqSNWrTpGX+W3JT4a1wyhK2euZSRW265BUOHDkUqlcKUKVPwxhtvUMW77777YBgGjj76aJ5s5UMKlvYGKAYNJaW00CoIhu2fjmnZ/mYRjIIgT+2VhZ301mkXrzKq2fQswHrl9v50T64UmgoBvZzWTc8yXdQy4udjK4EmEgqYlZH7778f8+bNw/z58/HWW29h/PjxmD59uucObStWrMAvfvEL7LvvvtzCdkV4/QLCSlcoQ1gw71DL4RTs4CdCtVKGxieCVR7G9F3TYo0ve2kvTZjwuIxwW5K6qC5SBpTONGIOZmXkhhtuwGmnnYbZs2djzJgxuO2221BZWYk777zTMU46ncbMmTNx5ZVXYvjw4UICq4IUPDahpb2KHn8ofCMov5ZpZaXZ6Mz7VFT7eLwYktLhylvS6GXvaMyWhopTe8PQhHPQiEJcvMfylhEfC8WvUBDqui/a9IyxK8yQbCpd1tfECpVWqlyKwMltTy8C09Le1tZWLFmyBBdd1HkkcSQSwbRp07Bo0SLHeFdddRX69OmDU045BS+//DK/tF0QHp+HML/o3Bt1MZiRLTH5MiwBhK0HDn/b/S7Om8ZDiFWe8DyrUp+Ks0PFpmesfLsjg63Naaxfvx7pSCVIe+cglTbSaG6Ooa2lFaS91TGNlubm/P10WyarILW3AwBaC+650d6aQXNzdpvXwvDNzdkt3Vva0lTp5GhtidiGb25uRsQwXMvU1go0NxtM+eXktNLe2kKVTktrM2KIoS2dcQzf2hJFs+G9m60bhBC0trZi/fr1iEQiRWfqsMCkjGzYsAHpdBp9+/Y1Xe/bty8+/vhj2zivvPIK/vrXv2Lp0qXU+bS0tKClpSX/u7Gx0SV0uOiCfVwe++HJy3LhfJ/GT8SrPmU7sAbq1lD0m83p1I0I41pduqWrbDLI9Hvy4zFl/SeclvZ2mMFdBJGtfIm4vdA7sIrRnCa4dfFm/KF/DdZu2YjWdGeKyVgEmcYktrW0Y/N25zNVjG1JrGvM9v9b4xEQAM1tHUrN1iTWbW1xjJtjWyKK1s3ZgXHdt537fyR2ZLemb0tn8nnQ0FYZx7c2Mse3p2AYhmuZdqRiaKqIm+TwIienlc3b27Ctpd0zPtmaQDIWRTpDsG6LvWKTaUwgFXfZl5+ByspKDB48GBG7cyQoUbrp2datW3HiiSfiL3/5C+rq6qjjLViwAFdeeaVCyXI47MAakDsrz26lYYR/wyaeeF0Xlc/cK20lDqxi0aXSlcqSw/EdImzlLVTAeCwln25qw8iRI/Gbu17HJwUbbu3avztuPm40Hnn7G/zhv586xr/txIm44tElAICG4XVoy2SweMUmANkzjK54bImnDPuP6o3LjxgFADj1Xy/kry/8+QEAgBUbmnDFo29Sl+ncaSNxo43MT52zHxKxCB5+62v88b/f2MY9ZtIgnLH/MJMcXuTktPLH5z/Fw297n6B7wzHjMXpQD2zc1oIzHrGftbjm+2OxyzD6cdmJaDSKWCwm/IHBpIzU1dUhGo1i7dq1putr165FfX19UfjPP/8cK1aswJFHHpm/lptbisViWLZsmemo4hwXXXQR5s2bl//d2NiIQYMGsYgaGGHspGRB44vgtqU5fT58Msn4Eg1yKsH9K5s9ncL0Cn1AaIaXqAJNSGqSPjwm9oMuWW6y4z215haXTpii7eCpYhUTjUaxqZngm62d0wD1zQSpVAptiJquF8WNJfP3N7cCrWnkf0diCde4ORpbDaRSKQAwhc9di8bbqNLJ0Ya4bfiKihTi0QhaiHOZtmciSKVSTPnl5LSyrT1ClY4RSyKVSiHeCsfwJJJwzCcImJSRRCKBiRMnYuHChfnluZlMBgsXLsTcuXOLwo8ePRrvvfee6dqll16KrVu34qabbnJUMJLJJJLJJItofDidTeOjZYRnB9GuMt9Ney6POY7lt0R5wgZv2Wydiq31xjGyyVYsfXV9sggiPG1RQg2PZWmvtfcLw9JemauwZKdDlVcpNZYAYZ6mmTdvHmbNmoVJkyZh8uTJuPHGG9HU1ITZs2cDAE466SQMGDAACxYsQCqVwtixY03xa2trAaDouqY0KXrNWAe9kBGsz4hlwBT0sXBazkszvuxSX82cH5M8wtMkjAlIX9qb8xlh94kKAmrlS+ZBeU6RS61TQOmJnJfXTe6QlYlZGZkxYwbWr1+Pyy+/HGvWrMGECRPw9NNP551aV65cKeTE4i8qTu1ldRQ0mUZcwnEKJBFe64VjOMfrnIOWrK8mOckIw2sBsxuoWZfqHjK2HhOH9MCSL7/1DsxJsHugeWdO4HxqbxCy87YHQghT3MIyB7IDqxRHdE/Tn3gmlKmEoe8uBbgcWOfOnWs7LQMAL7zwgmvcu+66iydLTYngtiLENR7nCyv7RWdNzjDkfXTzTkFNHdELr32+0TW9wr8JCNUqqJlTBpuUEWsHT/NsTdvB605ZKTI+gv1QPkSaQVBTHqXWdDsNI6VhuQP02TTSYR7MKOOGYd7RVgIBsRwHJw4/GkFRzOkEX9VZ6IxmuPPkPXHHSZM6w9r6jBQ4sAbvBiDcnv1YDZM9NI4/ftCKcg6Wxy11moYTKZYR8STo8qEQNizdSdgpb2XE6RSvkBPmTc8AvoGG19s/6L0cwrBAJBWPYvyg2qJ0zKtpOv8mlEs9y8lZmJUwnk3jOjBSymnddTYMfU2hBKXowBoENO0zbAshylsZUUCQG0Gpxv5YeavTpbMTJnU+7FFs8+YnHM9B6qZnjEt7VSBzgzrRJ0STP0G4jobw3ADQ4TrDbvBY+NE6pDNqDxD1Lod4vUm3SjmkF16fkXD0YSwo3fQs7Bgh2/TMjdJSWijDUQy2XktS5VcFW4KGTKeRorTd7zn5Y8g4mwawUzQ50nCJQ1Nt0mpWckPJr6YpkU6fts/43TPLTL+FLCOS6tw0lS3LMiInmdAjw4/IL7RlJGBK/dReL1N+VyiTfxk7/xSd+jL7jMgZ4tnrSV5bZ1bIZS/t5ZjmCgoCwi2LEstICfYKIjIHUd6wtD0WylsZcdr0zMelvdTpKkmVDVoZRJcAm3dVpUdGHYXpJWbaidbhb7drMvPnIUx1bQuFA6uvm2dxL+0VcH4NmQMr9eo8zzwkWW3C3oZdCJvs5a2MhACZ8+hBUGQJUT6Noh6/FaDOtAzn3zzOpC5ti2sTNVE/jxJsCyL4XlwFzooqHFhLsh0IyBzInjT5vEunsrUyUiKEoVHx+IJw5WPKkz6tUloSSAOLJ7xXPZkcWGXtiyISl7oticUXgbiaRnI+I+FHyDIiVRI65Fg4/XkyYeiXeQnbdFmZKyPBO7Dy+AWEYbkdPZRmVcqXumhpr4L3yW8FyCktYb8Kl7S4pm0C6LzC2tLD1Y1nUSKTiu3gBUQIyoFVSPEWiMudZwkpyznKXBnRsFHctGUs5S1O09945jTC+fqKOgqbl/bSDRKy60LUIdcpLVVkNz2zr6tS2schux08Z1y5ogCg8edQn0dXh6b8IWmeebQy0kFgG/1Q+ozwTl0EAa0fjCmcUxiXONnf8uuCzWdEXv5FKQlaaNzOPeKqtnA3O3dC/s6oJzw+I6x4OWdzpcmYkFA/E2DbK6Vmr5URjRDFX+wl1PptYJZeYXFdp1kY8+XxGZFdNFFn7dtPnMgfvwxO7XUqIQH/oBTEpmcacaiWnqsXg4nyVkZIpuAHuxlbBrT7jIThBeZxOnQtk8Nd3p1HZSlCQdW1dWAb2KOCPq5H2QvvBv+tywfrycOiZHdgtb8XhveRBW4H1kAai4uCR1sQj3B+fjQF2VRcyxmyNlzWO7BqxBH9YheOxxfNOT1W863k/Av5wR4D8cWGJkwZ1rPYNwcGvNQK89Re59+EeJ/a65VeNk22NII+HE8FriKFRF7as4hs45as6iqXkDxKakrRQq2VkYAxdRIhd4aj/igxmePZv3Jo49Omx0pwlhHz72jEwAWHjAYAbGtp94hMnzbt8BKCJueCeuEIIc6WEeW5y4OAT/kEBC0jnA3IPRpdmmEajMP6HoWpjoByn6YpoJT0/zA4lcmG9rVQvbSX3bFNbv6O+fDEoXQepk8v2M5LbvaSrDQh6s9VTPOGoacJg99KWBUKJ8LYPr3QykjAlFBbcVixoSAf+Uky5h+0BBSIrI6hdmCVWw+8Fi+vtPjwrgQCt6W93gKEpRUJfbsEsR28jDTCUvkIb38SpjoCyl4ZkX82jR8E/YXqDaVTbuHflGVSvXqHNT2lS3slple81bxYekEgVNeK3pmwDjRWuB1YJWojvDI4+T75Sei7XAdKSe4yV0aCJ/yKhReWQa7kywOmXtO3aZoiJcz9txtB+YxQukcxp0VFAB8YYXkXskt7A/AZsZGjHAnkbBqKPMPROjspb2Wk8NRek4Nfub427lCfmGnY/10czsGCwqkMSOn8A3xDxfZVKo5sqt+w9TzgGOh8LgMh3kt7S91p2guhnpBTk5Gxd4tXOPZVcyF9QB6UktTlrYyEgFJqLHbIPk8lDLCUwa/yMnWGHkFptwcvsr4IFlamchS01YHmefgtouOmZyJLe0M+ZV0KBNFS6dpnuHprrYxoqOHa9MzNMuJ4nXNJIFcs+WkEkXu4uhU6wrSnix0E3pYBN5nCNI6X0moa91VBfL5looRs3O6SlLUyYoTh1F6ORh6mrxVZ7yhtPRQt7ZWUvylNpr1N/OmluJbiOlwnrned8yxygvW5g5Zb10Gv5vGTgPYZkYSapb3+PcAgfUZ49nkKirJWRjRs0LRdw7D6grC3eL93cTWnEdwbKuYz4nW/M0AYBhjfHVh5oNj0LGwduh0i0zRBwGNNLQ5XQgXWACh3ZYQ4WEZ87K35Buuu8aKZi8FpflVQFWH0GeHCQTgC2iPlg7WEAGbFSSj7LvLOuBH2EvLKp8T6qSBNx7wCaHt5y4hbGF8koae8lRGNMPZnppgulBS04g7uWckWQWLe9nHpYwdlGQnXpmfeuFVTZ2cvRxCVxSGK03ekDBTAsFKKlqEyV0bsuxtfl/aWUJuhdh7jSM80ULHIZNpgTdLAQJHM0+fuKyUvlbidCE075cYaRza0y8TV4bADa0dtlMp4W0rWVLf3mNqJ3nPakkEglFb9FSL7SAiVlLkyohGlaJALwQAmAu0LWpnInjHpV/lkdq6yLCMiezXwOizbpSWUECdUS6N9bvzOS3tJKN7DELgqlQ1hUzRo0MpIwJRio/EiqD0kpG1AFVDXrfLrS+ZqnKDw+7m4bXqWI2x15EjJCOrlwMrpW8aZTmd4foLs493LGa5GUd7KiIMDa9hV+FAt7fWwhMie2lF+ai8MpjR9W9oreVUSl9zCSmaw8cNMke+VxLIG5jMiifD0dqVDKT7v8lZGNEzQb3oWjHNiKb6AhbDIb+2gWb4EaZVZL4VF5DmLPit/HFidvcfydeMiiMg3g+zvjUB8HhR8NNEXQ255hZbdh7RnCptyr5WRDgpnVX3d9IwnTthakQR4HdNk14RhMDrQ+vQoZBsyuNpdye5Pi/D1vApwKmFYDKm8T0DFk/OzOQS76ZlzmLC0ixxaGdEI4TVAdfUhQGb5mKaHin57PIeC29Sn9nrdZ3ZgFYgcAG6dtWH5N+yUipxhJWhFnJ1Sk7fMlZFwbAdfOo1G1rI62zguy1Dd85JsjmUVIMTIXtbHY5WSWpWBzDQ4LO0toTZCezBiWJCzk7LHffEsqAlv1YfLNFLWyogwww/M/lvRI1g5AkT2ev5SQ6Yy5Lq/gqcc9PnQmmdZtph3zMshPd99RmQv7aWQQ8TSJZOsA2sXfxEVU2r9WKnJCwCxoAUIFNEO6uBfAb1HA6MP406ilNqMXx1aoC+SUfodd+cUAp+1iTUfvwhkaa+vOaqjlAYnGYe7eQbr4j4jNHmHzWekvJWRAkjhnDrtU0pWAXudqUYgF8K0tNcLWfsC5PDj1F4W/PIZ8V7Z0olX66A/m8Y5j6xMjPFLYAt4WnJlCZPi6vjcg+ouLA+MV4zS6e3CQ3haJT16miZgwtTBeqHSZ8SSgmgCAjmz7TMSFjyVlYLbGR+naXjT9ozvQxshcNkBliL7sHwzEJSYb5rbPdp9iyQXVyS5IBVWt7xD0jzzlLkyErwDKw9h6lhk+YxwL+0NuCp8W9rLE8fN0sK1iZo5jv/TNCKRFTg6y0+2M30JlqscYdkOnlcGNVOMYagRdYRpjKClzJWR4OnqL0WpwbrPSFBWnKJNzzxjdIagdmBlWC5Mlx592p5p+VDtpTQdWi4E1VsKbXoWgNA0WYateZe5MhKyp9FFMJ3GSxun0NmSaRVCeShzPCszXOPwfHlbrVI+1P3gnpUF+fv/rB17iLzPiBxUFo0oTl82Upb2SlakS5VSKmeZKyPBU0qNxY6upgwYYBv0ZD4//76+qE0jEvM0p0cb93c/HofDd+uHB89scM37lH2GuSekaGmva5gQvRqByBLodvDScw4qYy7ofJrC9TFe1sqIEbKH0VUwHH9wxNcIwGdtUiNBMTSvX7/uFbhl5h7Yc2hP1zJcdsQY9OyWYJbPC68OO0wKhxOElNZHgwxZw7TpWZCUUjnLWhkpxOTAGnIlJUzySeuMOZf2yn7bDIPx1F6Zebtteua5WoZekuzgRCOPx2/Ghx+mAVF4NY/01RriCTr1CrxLuWVTykt7xayW/lc+TXsKQ70WopURjXTEO/oQ9JxdAJk7ntqmLxg3SAdYGn3e/WyaDp8Rl0KE6JshRGqgNzJWgZVSeVVSSn1pmSsjwS/t5TsjJDwNzNscKrfzUL20l3U1TVA+IyLZ0u47UbSUl6PuZb5JQqZ32Ut7w/MK5nESiYTEg5VXAiWKNLNVTyAvgbjceVL5jKiXg4UyV0Y0KgiTOV41QZWVtR8p7HxlTfOx78DKH5cmb9l9KwEJXYdd7oRApyopSqm6tDLSQVA+I6U+cMtaQsezHJg1LG16gTl7SvdDkB3fYikprW3PpKJaEpldUGD6FGeDllG3LMcnyEjPPS53VG4ikfC8K7RoZUSjEaBUvtQKxcxucy4+RAW66RlrBNlLew3zv25hwkCIRBGiq5RDNTS6SNh2Gi9vZSQENtgwdViyEHWclLXFPA/ZtIN5KLItDU71RL0Dq4TlkVJXG/n8WLKn9vrUR6j0hQq+m2PDh+fsZ1sKojeJ5DblK6HxpbyVkQJK6Wyarri0lzYZ67JUFvPphYeOZpKJhhJ61/Pwtp+iqmZ1AizFynIgv5omRC3A7amWct2HobeTUX03H7c79tu5Nw4e01dCau5QyRuGii1AKyMBU8J9hCO8ykJnfC8/FL5a22enOrrcu4zPSIEfDs+qLa/nwJ6kUGRzuxLJnA63U3tp5A/LN0PYP66suLY7ah80ObLIINdfHTW+P/7xk8noUSl/cz6nPMO08tKLslZGDNPS3oLVBiF/ecPUwGRJwnJqb+F8aNA1EaZnQQvvkfLWGH77yAnVtccyZebkJKXjB7Sb3CnJWAJKlvYyploKz7kQOp+RcFHWykgYKMXBzBOJSzjtk+f00qeIxrrPSJhxXEory2dEYAdW8TpW/5QI8W9pr+rSdJV+JkzTYrQEspqmBJ93mSsjBT0NR2etUeMz4pmmYmUnKMJelKJN0PzO3+f83MibwQOWoxAnWUqtO/Pj1F7WBxeWAzFl5hGWacQcZa6MBE+YOjMVqDGx8sbzjmmA7SsyjMqQXSdTWHbapb2eOqFAhy76pc4cvcxP7e0qlGKdBiFyKVrCuJSRW265BUOHDkUqlcKUKVPwxhtvOIb9y1/+gn333Rc9evRAjx49MG3aNNfwvhI21bCLoNqUqnZpb3Avsays2zKZbHpyknOE5jnL3HzJ96W9cPYfo9lnhAWVZQts9V2gm56J3S8KL/Ft8qMdl8U+I/fffz/mzZuH+fPn46233sL48eMxffp0rFu3zjb8Cy+8gOOOOw7//e9/sWjRIgwaNAgHH3wwvvnmG2HhZVJKZ9OEaWmvV9fBswOrG9bj0Fk6CWpZqFMM55dae9rWNJKHe2mv54VifrDHQAyr64aTpw4NlRUwVK+QD4Sp7lkpfFQlWY4AOomy8Bm54YYbcNppp2H27NkYM2YMbrvtNlRWVuLOO++0DX/PPffg7LPPxoQJEzB69GjccccdyGQyWLhwobDwmnCi4tyRQlSu4gj2FZaTe1uHMqLaykOTelUyhud/vj+uOGpX5rjF+cl0gKWAOCstectICQyPpaZ3yWi30p+K1ATVtxkaZSRsCjmTMtLa2oolS5Zg2rRpnQlEIpg2bRoWLVpElcb27dvR1taGnj17skmqAPPSXhT87adlhGOJZUBar60vggIHVtdwhvlFYzrpltpKw5BmCAej9o5pGie4l/Ya7r+d40me0+BB+tLe8D33rrrpWZj8zsKeVz7PEnzeMZbAGzZsQDqdRt++5h3k+vbti48//pgqjQsuuAD9+/c3KTRWWlpa0NLSkv/d2NjIIqYmYJSvuVDqM6I2vB9p2U3TmBaLUeraXsFEBmSuTdgkOsDSQEBRBy5ihOXL0zq1WcqUomOmldCsplEvBhO+rqa59tprcd999+Hhhx9GKpVyDLdgwQJ07949/9+gQYMUSeRgGQlLLxIy7Bq4tPeqcKBRtPMnbYdc6h13W9rLMsLrMyLXulDKhLHsIRSJC38cWNlyEZFJ7zNCB5MyUldXh2g0irVr15qur127FvX19a5xr7vuOlx77bV49tlnMW7cONewF110EbZs2ZL/76uvvmIRUxMwyn0VFKZvMG4Hz60YKVTs2jM2lpGCDAkJaGmvQFy3tKgI4AOj1Kf7woiuJTrofEbC9dHNpIwkEglMnDjR5Hyac0ZtaGhwjPfb3/4WV199NZ5++mlMmjTJM59kMomamhrTf0oI17MoSWQpBiznqPBmGaaPBZWipDM5B1aFmcD/AdT3pb3ZY3sdZAlRY+piSNn0zCMR1ixEnneRRZE7JZY8Sw8mnxEAmDdvHmbNmoVJkyZh8uTJuPHGG9HU1ITZs2cDAE466SQMGDAACxYsAAD85je/weWXX457770XQ4cOxZo1awAAVVVVqKqqklgUMfSpvaVB8am9ctNn9xnhE8AwjKKvdbUWn054Ww+vA6tdeFFFJiy6QMkoJSUiph2mpb0lWI4gZC4Ln5EZM2bguuuuw+WXX44JEyZg6dKlePrpp/NOrStXrsTq1avz4W+99Va0trbiRz/6Efr165f/77rrrpNXCk2oEH33vJf2lmCPZMGfr6PSr6dC/C6P66ZnvkpSonDvZ2Ng/KBaAMCxe/L5C8p+PjLT88eBtfRaKLNlBADmzp2LuXPn2t574YUXTL9XrFjBk4VP2L8s1g7ovUQCvZvWoL6bu1+MX4Rqaa+ktFmW3fJ+YbMsH5adpkgeMrAelCejDbEqhYEqR5IrvHOfEbnpieC6tFc8eV954Iy98PW3OzCidxXu/t+X+euy2pCf719Y6z5scumzaTz4LB7H8QPq8d2Hvhu0KCWD+hc9RK8Rr/+KD2VQ7jMikL7w0l4f6o+4bHpGGz8sWOu7rirpf6YMJGNRjOgdnml8qUv4Q9B/jRvYHXvvVBe0GCa4LCNdBadNzwp/vJtM+CZP2LFdASLpvTL5gXi8rIU7sMre9MwwwtFZqIR7aa/VZ0SCLCL5B0mujYRLJnpuP2kivm1qxSl/X6xMHm6krO31uu3fgwtTGwGA3/5wHI7hnP5SibaMaKRTygfldSWcFDzapb2e6bNO08j8uvShDbgpbbSKbViwvpMRw0CPbiX4oRVQnZbaQXlhzt+J8lZGwmRHLVFUzOF6Lu3lPqPEOzTrPiPlRBBLFN3y9wOnHsKw/Bt2IpaePsxyS1na67Vxoq8+I+Gq7ajKw70EKG9lpABS+OUYukVPZsp+aW+I3qUQieJKYZ3Jaj1im575XHMhf2dU14bV2Tg7HVkalPrSXitBFyGsqxG1MuLGPucFLUFpItjWvaLzvkzUPiMBTwHwYv3gcSqHPGXW304tCP3Fqa46z/4LZ8duxU7OsMouxWVEctFE0ouEzBIR0sde7spIgQNr4ZdjrgPquytw+A0+y+RNqJb2SnNgpUuoeOOtYN+soPMvJBZ1fp0tK3t9PbWXN7xUpGcenueew03FjBY5H4dPficMh79LhVjIlBFtGSlVYiXo5BUwYV1SShONdTVNmF7ruKXTU35GkM9xzdM8AplT4rIbfIFlxD1+WCjlaZowIFJXVh+NoD9gtDISckppO/iuCIsDa+HLFM7XKhhcHdNMlj++9EUtIxq1uD0O+2kadbKI4DZY0w7kXqH8LHvYLCMuBtRACalYmlJG9avHf1Ce/NU0vLKo+GpOxMyvs/BzYFjVRJVcoRLJIZxZYQ14es7yb9gJpLrCZBqixLGeBOovbKtXgn53nChrZcTQFhAmlG56ZkrHY1megvy7AjHr+k3F+F/3ASzt9enUXtUDhJ1zc1j9RvxwYHUqe1TBc4hZHXYCRk/ThJxSmqYJ+9JelR0rkXSuihOsc+lh6tCLOr1CS4JFTp42ZI0itB28YL0xx1b0zritlAhTn283ACmXT1IGpqW9UlJ0xsmKIdJe7fx17P72i5AZavJoZYSBsCsBQaBCMfD+quFMlzOeClS0pLjPk8F+K2L+L+11/izJiaLiS1oFRQNiqN4GM0FueqZiSsXNYhnEU9CWkTDipFw4XlarjIyur6EKF6alvfKgdEwz1PoOGGDrDAN7r22eRVXS+agpGUuii6IwJhGmpb2iouSSk7WHhOqqscoZ0vHIFj+nZZ2US5F8i1bTBKwIhm3fkxxlfVAeK2mSRsSQr7+9c/nB2NGWRs9SPCvCBtWdB68CUkodMA83HDMeZ9y9BD89aCcA6js9kdQNg1259dtXiACOQuaVkRJpVLY+I6Uhuq9EFfh3uPmMGDwvgiAh1UXKWxlxOrXXyQKiapqme2Uc3RFXkrZqZLVrk7XDI6zKlykrR0jf1kJsRBzZtxrP/+IA2uDiIoRsNAtiGjWsyyStlIrSBLgr0bybIxanY4/TMlyR2iveZ8Q+3SPG9cP21jSOGt9fIDdvwtoWyloZoaGwg0uTdICSlA7KNz3j7BpUWArCNiAXIiyax9jOmrz1gEP2HVwL44sVjkZtcd30rCN/t449TC5mRdN0gU8WhBMVA7XbPiOF2VWn4vjj8XtIz98tzzBRInq9Kth6C+3AWoy87eAL0+RzPpMlSVhf1rDhdz2F6rF0CBO2PSScsN2BNaSiu8lFK7NXMKc+RsUGZW4KThAqYVgtI2WujHTiNE1T2GgzJOOjRM6EXSlS+YKJFF2J/wpnPD+eoZuCR5W/pXBFS3tZ5THs/+aBOb7k+qZZTROmPr/IZyQYMbjws7dzcu4UsYBafUYMhx9+tZewKtBaGWGg3KdpbDc98+jWVIy5YdpnpJzxe4rK96W9IJ7tV9rKBMVlKyXLiBS8rKsO11VYRlynaaTn5k1IdZEyV0aIgwOrQw8UFotEV1zaS21+NcL1MnFvBy+aMY1hw+WrS8qpvazxmXOUiKIdU0tlnxHbs2lUPxFJHYafNexsGeFPM2rZZ8QpLb/KGVY/t/JWRijQDqzueG0Rr2Zpr/w082mD7WUN6XvtD0Jl51CGLA6wRfclP4ysA6vD0t6Of8Nq8rZSLGZpyO03SraDd2kjQXzeap+REFL4SJx8RjLI2F7XyIXlK417NU0430FfUFH0sHZqNMiSPEwbSLn1ToFM0yjYD0ieA6v9deft4PkpXtrb+bu1PVNwXSATBkLUZE2UtTJCQ6FlJCwOrOWOUsuIwabq2IWurQzHnjFh2/RM1GJmiuNDh0rgdlBe9l+3jp1llkJ1cewcWEM6JgWKmu3gw1XTYf2IKHNlpMBnpOABOU3NaGWkGFnNmm0L9nC+TDkuP2JM0CIUUWrTZTz46dOVU/TC5DPiJknY35lCfNn0jHFpr0j1FW3Fz5+UFLQyUqIUKiBhUUbC4kjrhMq2Tgj/y0zTIWd9RljSpLsWBG5ySDm1l/FJePl88KZFheylvTnLSIme2tunJhUq+dwIw9JeEWgtI2mfhheXc/sCJaRihYdCBaTcHVjD0nmFdYdXlvgh1yep8H3TM5/zI8T7Ocky61sVZdlTbIVivnj+AR2HKobkhbYgx2eEr2zOioN7egfvWu94j7aNpDP+aCPaMhJKvEeEQmUkLBaJsC/t5ekIaGNkl/ZyOsZRpi+aZljedTfjsJSlvax1FWS9yF7a2/FvWDt2K4Vf/EN6dQtEBt6q8rOGeZ9n/9oKPHhmg+29mNUU4ZCFb5aRkDbZMldGvNEOrOEjVIsAghqMQtGh8AvBpwwVTPP4Un6X9XMd+ZfK0t4wTCeG5FvOFbcTdr3oXmHvuB42y0hY/YfKWhkxnDY9g3Zg9Rs/9vagiVcyx4cxduzl4MAaBGFyYGVZ2guERJ+1wdURlzYNzsI5WUZEHrN16sepj0n7pKyFqc0WUtbKCA2mPUe0MkJF2H06ZGI/TRMS+RTLIZI6T1zD4W9VZH1GnDY9y0rg5gwYJktAiRhwAkdkGa7T86Z1is1k/GkwYZ1ajAUtQLB4bwefznRaRsrdgTUsqN1nREIa4klIR4VMIe3TpLGxqRUbm1pt7+XKrmqahmeDRTdJbC0jIX2AbnLRysxbMhWbnhWl5egz4o8yEtLHri0jXoRxB9awONI6obKtE+KD5aWrLO11uUfThqwWKOGlvRJ9PpgH0pCf2qu+TZfONI0VP3s7EeVS9Bm2+2UZCamZTCsjHmgH1vARpmkaO8Ion4qv4LAoXUESVuuClZCOP7YE6TPiaBmR+JydUvJvaa8v2TBT1sqI4TRNE3IH1rB3gDKWjbqFU71pD5MzrW18ebKI4L5fg4SlvazxmXOUiOylvR3plcpqGvtpGn9l6MpLe2WgHVg1rmjLSPiQbXm499QpnWkzJl3K5m9RRJRi0bNp7KL7ZOXO5p/zGQlpx27FTmcKowXPC9XV7bgdvMQ8nH1G9NJeDUrLMtIVYTq1V/K7NLhXpUUWMcLyrsvcft02fWbFTYEQBbT5tWsUCjY9K5EeNKwDkB0sVlLne3zlDdKfwi8H1rAa80rkVQoObRlhJ6Rt3RGzYyWjU6ZsYUoIkS9rA4aQT6ndY2qntHOP6VfDn7EFVdM08reDD36aRrbfvQrxnTYu86Ou/FNGwtlrlbUy4rTpWeGPMB6U1xVhW3kgef5fQBa6FOnoXZ0UzdiRctj0rJXSMvLo3L2F85I9TaO6KsP6NWyHjKW9vJxz0Eil6QMum55pZUTjhp6mYUf9pmeS0/PwRaCN63bNjVcv/E7B4WXycJNDzqm9jPJYfoi0E5FTe+NRGd1eNv+wLpO0EtYBSAQVikltZQI3zphQnJcPNlDflJGQjvplvumZN6YdWKGVkTAgu/+3djTip/ayMaC2Qig/GlR0pl1wfGOmVBxYw7wfjgrCXDYn2XzbZySklRNSHckvCh6+UXjVfgfWjE/ezqUO17JRhrCyXyaTZYTVKdNG8rA4C7ru1yBjaS+rf02hbw5z7tbEWMOraTOyLCOq24ydb4vf7TSo12LmlMEAgJ99Z6dgBKCk3KdptGXEA20ZCR+y36XiQZYlMtWlwFHiMyI/SY/8wlOzOUlKeZ+RUoe2RFd9byxObBiCUX2r+fOSWH1OSent4MuaQgfWzifktIJG+4yog+0Fke3Aatj+zZ1eSF525XKEbGmvn+SsCiWiizjsM6KWSw/fRXEOzhS+x9GIgdH1NYFYLF/+5YHUYdM+HfMRVsW0zJURb7QyIkYY9rdgTU/QMNKlBl03hJb2ClZSWOpYVcfeqyohN8EAlvbuObQnPr76kPxv6Ut7Q9IGnBhdX41BPSuLbzgI7tepvWG15pW1MlL4SJw2PdPKiD+IbsEulHfB3zIOQwzLdILqL8GwDwYqUT1Nc+OMCdhreE/cNXtPKekFNf6k4lFlabu9Z6XYNv2zjPiSDTNlrYzQoJf2hg/5lhF+00gpdnpA8Et7fa822af2St5nxMqQXt1w3+kNOGBUH674txy/h+m37aZnIVGaSwGZjzkRddhnxKfDacLiYG9FKyMe6B1YxaB9vQJdTVPwt08fJ75TDpueBYG01TRSUunk8HH9TL9tfUa68PMLc9EqEvbrRvxa2htWylsZcdiB1WkFjV5NEw74T/+0j2hVbpjOySmV5TQW5Jzay+/BKjoQMiuN0hXY8D/k/XbuDQDYfXBtKJwWQyACNwYMPHRmA3bpV4OxA8SOE6hwmLrKdNUvIUrKWxmhoHBvEb3PSDjgHQgc/UFMe8wwymIjCo3p3vfj27vApmdhGszCJIsTNx87AVccOQZ3nDTJYZqmxPG5AJOG9sRT5+yLSUN6CqVTmbBXRsrdMlLW+4wYcLCMEG0Z8Z0A/TQK0yOECKffLenttFcRj2J7a9oznAit7WrbaykfKhg2ZUKFPLWVCZy89zAADluAh6wOZKJ6xZ0ITk69fu0zEla0ZcQDvZpGDDVLe8PjM2InSjeKM2acTLUyScQ6X+9hdd2kpy/yFEphmqMrEYZpGtmUaokqHCwj5a6MlLVlhMYor5URd+Q58AW4tLfIZ6STaMRg7iS6OTiodebX0SE1MSVrScQ7yA/2GIDlG5owc8pgxw5QhC44vlFTamW3U+C7skIY5rI5TdOUuzKiLSMeFCogPMshuzp1VUn8eOJAX/OUboJ1uVdbEfeIWxzbqbPJEY9EhPdf+PvsyaitjOPm43Z3DNOvewWu+/F47D64h1BeTrB2+CJnAIUNmrKHqbsIw2oa2fXRImka8sz9R3iGoakr2vI5WUW1MlLG0LyLhcpI4Z4j5YjTy/K7H4/3VQ6VB+Vlf3de6O6hjNhROE0zsk9V0f141BCepmkY0QtvX/ZdHDW+v1A6IpS6QiGC/LKrrUyvdyYW1p2wuiBOVspyd2Ata2WkEL0DqzebmlqDFgEAf7ft9DVbeN36ddO90sMyYpNkssBX4+jdBxTdj0UjUqZNgt5hlX3Ts4KlvezimAlJv/3V1q8QSa6xvcf7ePYcKrZaww6vs2lqKyVvP2+Dn8prU2u71PRoLGG05fPDXyzI/HjRyogH5ayMnD99lOn3jjZ2y1B1it2y4InK1TSmIxOBHhyddKGS0NRS3CnGoxGuDiJslghReX64R3Z6b/fBtXT5iWUnFcPITtse9q/D0G34jTCi24TTfPmXB+IvJ03CtF34dl11w9ZnpOBaz24K3tMA2by9LWgRHPGaxpVNdao0XEO5lJFbbrkFQ4cORSqVwpQpU/DGG2+4hn/wwQcxevRopFIp7LbbbnjyySe5hJWO06Zn+tReAMCcA3fCR1cd4h3Qht/+cBzOnz4KO9lMU4iicmWA1TLi5TPihd3yXd5pml7dkkKyyMbJMjO0V/ZwsP07Nt3qDG8Od+60nXHnyZPw959MViKfG6L+CwYMbGzemP8dSa4VlAgY1LMS3x3Tl9viNXlY1qJiN7Xo9c7wKN1hZssOucqIH0t7VdFllZH7778f8+bNw/z58/HWW29h/PjxmD59OtatW2cb/rXXXsNxxx2HU045BW+//TaOPvpoHH300Xj//feFhfeDwr1FZByiVmpUJKKmaQdajtlzEOYcuJMCieR9IdekYrjsiDFF19vSnc+8pqBjH9ijolgWj15qu8VcXJOK4ZaZe3B9HdUJnuS678g6AMD4QbVC6Xhx3+kNuOjQ0bj+GBdfIiO79Pg7o/uiRoX1zAe+3vp1/m8j1higJFl+96Px+NlBI/HY3L2L7nlN0/SuDpei6wbN4YSbt4djStkOp/5UldKgxDqtAObS33DDDTjttNMwe/ZsAMBtt92GJ554AnfeeScuvPDCovA33XQTDjnkEJx//vkAgKuvvhrPPfcc/vjHP+K2224TFF+MzZEMVsWyg8LmaGcDac20YtW2VQCA5vbm/PVvm7/NX3fDiH0LANieqaQKT0su3ab0eqza1l1KWpnIDk8Ze3Zvwpot2XpwCptLz4h/i1Xb6JW2XLxt6fX5v9dtX43ktsqiMADQZrSjmbTkr63atir/97cta01hrazdvhrzDqnDH57/DDcdtzvGDeiOSMTA6qbONNZuX42ByQpMGWkgFY8gGv82f49Em2HEdpjqoRUb8vdn7jUYPSsTJpk2tsRNMj0570BEIjuQiW0ylYGG6qqtzHEKueSoejz9PjBtTF98um6NbV01k0Q+7Y3N35rCrN+xBjXbOqcjtqXXFaWxatsqIAocObEC2zMbsL1g9mJg7+Z8+B3pDVi1jW3A2NbWVlCva9CUMSt0x+zVDQ8u/tp0LVeWNmNjXmFYtW0VSGwTjBj/gLW+eTW+3PFB/nc0tQrp7UNNYUg05vmcOttWgqlvsWMH2YBjG6oAbMGqbVtM9za3bixqO982teavHTt1JJ74sDPtXx09Fpc+wv/BWFiWXB5tRqbj3djsWg5rPbQUvGOrtq1C96qt+NZjGmZDc5q67gv7nlXbVmFza3E/sn7HalRt2wog2//avYfrdmwzxWuPtHr2l4XcNHNPT5kL83ajMJ1kaks+zqFj+7nmUVdRh0Q0GCuZQRjWq7a2tqKyshIPPfQQjj766Pz1WbNmYfPmzXj00UeL4gwePBjz5s3Dueeem782f/58PPLII3jnnXds82lpaUFLS0v+d2NjIwYNGoQtW7agpkbsXIBCfnj7eHySLK+pF41Go9Fo7PjnYf/E+N5yV0c2Njaie/funuM3k2Vkw4YNSKfT6Nu3r+l637598fHHH9vGWbNmjW34NWvsPdABYMGCBbjyyitZROMiCgPJDAGJRGFk2tHSsWdyKpoyhWtOZ60CySidKbM9Q5DOECSiEalzjTLTJcj6xtD4XxCSnbqIRgwqEykL7RmCTIYgEYugLZ0BIeadQ3Nh2tMZGMjdM0zytKczyHTEyxCS3wY9Ho0gnSHIEIJoxEA86jzdRDr+z1odBNlt1aOGgWgEaEsTUz0QArSmM4hZ6ibd8axyeVrD5OJFDQMxhyPFi2TMxYkYUpZitrZn8odzRYxs/VjLnwsTMYyi5wJkD/ciJPuM4lEjFDt9ZuXJ1lNOHkIIWtPE9AwyBe2/sN1EIwYIMR9cZhjZ9RQEKGiHQEu646OJxE2+Zgayq6a83pd0hjDVXa4PyJUpFjGQJqBqE63tGUSMrFw5Cp9/Ns2s8EaHbLkpy9y7hI46iBoAOuokJ397OvuuxS3lzpXR2m/l3vfCr+GIgaL31PqOFT7f9jTJPxMgWw+09ZnOkGwdRiNobc/AKMg799uAAQJiksntPcy9L6zPPxE1qHyFcnnn2mKuD49GjPyzSkQjps0oWfrvIDeLC6Vny0UXXYR58+blf+csI7J54PSl0tPUaDQajUbDBpMyUldXh2g0irVrzZ7ja9euRX19vW2c+vp6pvAAkEwmkUyWjkOVRqPRaDQafpiWSSQSCUycOBELFy7MX8tkMli4cCEaGhps4zQ0NJjCA8Bzzz3nGF6j0Wg0Gk15wTxNM2/ePMyaNQuTJk3C5MmTceONN6KpqSm/uuakk07CgAEDsGDBAgDAOeecg/333x/XX389Dj/8cNx3331YvHgxbr/9drkl0Wg0Go1GU5IwKyMzZszA+vXrcfnll2PNmjWYMGECnn766byT6sqVKxGJdBpcpk6dinvvvReXXnopLr74YowcORKPPPIIxo4dK68UGo1Go9FoShampb1BQbs0SKPRaDQaTXigHb/12TQajUaj0WgCRSsjGo1Go9FoAkUrIxqNRqPRaAJFKyMajUaj0WgCRSsjGo1Go9FoAkUrIxqNRqPRaAJFKyMajUaj0WgCRSsjGo1Go9FoAkUrIxqNRqPRaAKFeTv4IMhtEtvY2BiwJBqNRqPRaGjJjdtem72XhDKydetWAMCgQYMClkSj0Wg0Gg0rW7duRffu3R3vl8TZNJlMBqtWrUJ1dTUMw5CWbmNjIwYNGoSvvvqqy55509XLqMtX+nT1MurylT5dvYwqy0cIwdatW9G/f3/TIbpWSsIyEolEMHDgQGXp19TUdMkGVkhXL6MuX+nT1cuoy1f6dPUyqiqfm0Ukh3Zg1Wg0Go1GEyhaGdFoNBqNRhMoZa2MJJNJzJ8/H8lkMmhRlNHVy6jLV/p09TLq8pU+Xb2MYShfSTiwajQajUaj6bqUtWVEo9FoNBpN8GhlRKPRaDQaTaBoZUSj0Wg0Gk2gaGVEo9FoNBpNoJS1MnLLLbdg6NChSKVSmDJlCt54442gRaLipZdewpFHHon+/fvDMAw88sgjpvuEEFx++eXo168fKioqMG3aNHz66aemMJs2bcLMmTNRU1OD2tpanHLKKdi2bZuPpXBmwYIF2HPPPVFdXY0+ffrg6KOPxrJly0xhmpubMWfOHPTq1QtVVVX44Q9/iLVr15rCrFy5EocffjgqKyvRp08fnH/++Whvb/ezKLbceuutGDduXH6DoYaGBjz11FP5+6VcNjuuvfZaGIaBc889N3+t1Mt4xRVXwDAM03+jR4/O3y/18gHAN998gxNOOAG9evVCRUUFdtttNyxevDh/v9T7maFDhxY9Q8MwMGfOHACl/wzT6TQuu+wyDBs2DBUVFRgxYgSuvvpq0xkxoXqGpEy57777SCKRIHfeeSf54IMPyGmnnUZqa2vJ2rVrgxbNkyeffJJccskl5F//+hcBQB5++GHT/WuvvZZ0796dPPLII+Sdd94hRx11FBk2bBjZsWNHPswhhxxCxo8fT/73v/+Rl19+mey0007kuOOO87kk9kyfPp387W9/I++//z5ZunQpOeyww8jgwYPJtm3b8mHOPPNMMmjQILJw4UKyePFistdee5GpU6fm77e3t5OxY8eSadOmkbfffps8+eSTpK6ujlx00UVBFMnEY489Rp544gnyySefkGXLlpGLL76YxONx8v777xNCSrtsVt544w0ydOhQMm7cOHLOOefkr5d6GefPn0923XVXsnr16vx/69evz98v9fJt2rSJDBkyhJx88snk9ddfJ1988QV55plnyGeffZYPU+r9zLp160zP77nnniMAyH//+19CSOk/w2uuuYb06tWLPP7442T58uXkwQcfJFVVVeSmm27KhwnTMyxbZWTy5Mlkzpw5+d/pdJr079+fLFiwIECp2LEqI5lMhtTX15Pf/e53+WubN28myWSS/N///R8hhJAPP/yQACBvvvlmPsxTTz1FDMMg33zzjW+y07Ju3ToCgLz44ouEkGx54vE4efDBB/NhPvroIwKALFq0iBCSVdgikQhZs2ZNPsytt95KampqSEtLi78FoKBHjx7kjjvu6FJl27p1Kxk5ciR57rnnyP77759XRrpCGefPn0/Gjx9ve68rlO+CCy4g++yzj+P9rtjPnHPOOWTEiBEkk8l0iWd4+OGHk5/85Cemaz/4wQ/IzJkzCSHhe4ZlOU3T2tqKJUuWYNq0aflrkUgE06ZNw6JFiwKUTJzly5djzZo1prJ1794dU6ZMyZdt0aJFqK2txaRJk/Jhpk2bhkgkgtdff913mb3YsmULAKBnz54AgCVLlqCtrc1UxtGjR2Pw4MGmMu62227o27dvPsz06dPR2NiIDz74wEfp3Umn07jvvvvQ1NSEhoaGLlW2OXPm4PDDDzeVBeg6z+/TTz9F//79MXz4cMycORMrV64E0DXK99hjj2HSpEn48Y9/jD59+mD33XfHX/7yl/z9rtbPtLa24p///Cd+8pOfwDCMLvEMp06dioULF+KTTz4BALzzzjt45ZVXcOihhwII3zMsiYPyZLNhwwak02lTIwKAvn374uOPPw5IKjmsWbMGAGzLlru3Zs0a9OnTx3Q/FouhZ8+e+TBhIZPJ4Nxzz8Xee++NsWPHAsjKn0gkUFtbawprLaNdHeTuBc17772HhoYGNDc3o6qqCg8//DDGjBmDpUuXlnzZAOC+++7DW2+9hTfffLPoXld4flOmTMFdd92FUaNGYfXq1bjyyiux77774v333+8S5fviiy9w6623Yt68ebj44ovx5ptv4mc/+xkSiQRmzZrV5fqZRx55BJs3b8bJJ58MoGu00QsvvBCNjY0YPXo0otEo0uk0rrnmGsycORNA+MaKslRGNKXDnDlz8P777+OVV14JWhSpjBo1CkuXLsWWLVvw0EMPYdasWXjxxReDFksKX331Fc455xw899xzSKVSQYujhNzXJQCMGzcOU6ZMwZAhQ/DAAw+goqIiQMnkkMlkMGnSJPz6178GAOy+++54//33cdttt2HWrFkBSyefv/71rzj00EPRv3//oEWRxgMPPIB77rkH9957L3bddVcsXboU5557Lvr37x/KZ1iW0zR1dXWIRqNFntFr165FfX19QFLJISe/W9nq6+uxbt060/329nZs2rQpVOWfO3cuHn/8cfz3v//FwIED89fr6+vR2tqKzZs3m8Jby2hXB7l7QZNIJLDTTjth4sSJWLBgAcaPH4+bbrqpS5RtyZIlWLduHfbYYw/EYjHEYjG8+OKLuPnmmxGLxdC3b9+SL6OV2tpa7Lzzzvjss8+6xDPs168fxowZY7q2yy675KeiulI/8+WXX+I///kPTj311Py1rvAMzz//fFx44YU49thjsdtuu+HEE0/EeeedhwULFgAI3zMsS2UkkUhg4sSJWLhwYf5aJpPBwoUL0dDQEKBk4gwbNgz19fWmsjU2NuL111/Pl62hoQGbN2/GkiVL8mGef/55ZDIZTJkyxXeZrRBCMHfuXDz88MN4/vnnMWzYMNP9iRMnIh6Pm8q4bNkyrFy50lTG9957z/QiPffcc6ipqSnqZMNAJpNBS0tLlyjbQQcdhPfeew9Lly7N/zdp0iTMnDkz/3epl9HKtm3b8Pnnn6Nfv35d4hnuvffeRcvpP/nkEwwZMgRA1+hncvztb39Dnz59cPjhh+evdYVnuH37dkQi5iE+Go0ik8kACOEzlOoOW0Lcd999JJlMkrvuuot8+OGH5PTTTye1tbUmz+iwsnXrVvL222+Tt99+mwAgN9xwA3n77bfJl19+SQjJLteqra0ljz76KHn33XfJ9773PdvlWrvvvjt5/fXXySuvvEJGjhwZmiV3Z511FunevTt54YUXTEvvtm/fng9z5plnksGDB5Pnn3+eLF68mDQ0NJCGhob8/dyyu4MPPpgsXbqUPP3006R3796hWHZ34YUXkhdffJEsX76cvPvuu+TCCy8khmGQZ599lhBS2mVzonA1DSGlX8af//zn5IUXXiDLly8nr776Kpk2bRqpq6sj69atI4SUfvneeOMNEovFyDXXXEM+/fRTcs8995DKykryz3/+Mx+m1PsZQrKrKAcPHkwuuOCConul/gxnzZpFBgwYkF/a+69//YvU1dWRX/7yl/kwYXqGZauMEELIH/7wBzJ48GCSSCTI5MmTyf/+97+gRaLiv//9LwFQ9N+sWbMIIdklW5dddhnp27cvSSaT5KCDDiLLli0zpbFx40Zy3HHHkaqqKlJTU0Nmz55Ntm7dGkBpirErGwDyt7/9LR9mx44d5OyzzyY9evQglZWV5Pvf/z5ZvXq1KZ0VK1aQQw89lFRUVJC6ujry85//nLS1tflcmmJ+8pOfkCFDhpBEIkF69+5NDjrooLwiQkhpl80JqzJS6mWcMWMG6devH0kkEmTAgAFkxowZpj04Sr18hBDy73//m4wdO5Ykk0kyevRocvvtt5vul3o/QwghzzzzDAFQJDchpf8MGxsbyTnnnEMGDx5MUqkUGT58OLnkkktMy47D9AwNQgq2Y9NoNBqNRqPxmbL0GdFoNBqNRhMetDKi0Wg0Go0mULQyotFoNBqNJlC0MqLRaDQajSZQtDKi0Wg0Go0mULQyotFoNBqNJlC0MqLRaDQajSZQtDKi0Wg0Go0mULQyotFoAuOAAw7AueeeG7QYGo0mYLQyotFoNBqNJlD0dvAajSYQTj75ZPz97383XVu+fDmGDh0ajEAajSYwtDKi0WgCYcuWLTj00EMxduxYXHXVVQCA3r17IxqNBiyZRqPxm1jQAmg0mvKke/fuSCQSqKysRH19fdDiaDSaANE+IxqNRqPRaAJFKyMajUaj0WgCRSsjGo0mMBKJBNLpdNBiaDSagNHKiEajCYyhQ4fi9ddfx4oVK7BhwwZkMpmgRdJoNAGglRGNRhMYv/jFLxCNRjFmzBj07t0bK1euDFokjUYTAHppr0aj0Wg0mkDRlhGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIGilRGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIGilRGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIGilRGNRqPRaDSBopURjUaj0Wg0gaKVEY1Go9FoNIHy/wEK73ZWSng2YQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ep.plot(x='t', y=['mcull', 'wcull', 'restoration'], title=f'{ep_rew}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d552ce2c-6d6d-4af6-a703-c58af8d61e42",
+   "metadata": {},
+   "source": [
+    "## Reward distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "59a0b8ea-81cb-4ad7-8565-0d687fb2290d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 20:49:40,491\tINFO worker.py:1749 -- Started a local Ray instance.\n",
+      "2024-05-20 20:49:49,560\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    }
+   ],
+   "source": [
+    "caAgentRews = gather_stats(\n",
+    "    manager = constAction(res.x), \n",
+    "    env=carib(), \n",
+    "    N=300, \n",
+    "    return_ep_rewards=True,\n",
+    ")\n",
+    "ppoAgentRews = gather_stats(\n",
+    "    manager = ppoAgent, \n",
+    "    env=carib(), \n",
+    "    N=300, \n",
+    "    return_ep_rewards=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "69e748a5-db7c-426f-aab3-afecc4eff77f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ppo: -38.17, c. act.: -59.44\n"
+     ]
+    }
+   ],
+   "source": [
+    "ca_df = pd.DataFrame({\n",
+    "    'rew': caAgentRews,\n",
+    "    'strat': 'const_action',\n",
+    "})\n",
+    "ppo_df = pd.DataFrame({\n",
+    "    'rew': ppoAgentRews,\n",
+    "    'strat': 'ppo',\n",
+    "})\n",
+    "rews_df = pd.concat([ca_df, ppo_df])\n",
+    "print(f\"ppo: {np.mean(ppo_df.rew): .2f}, c. act.: {np.mean(ca_df.rew): .2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "17deda7d-6b0d-48db-b7ac-f4758f8d520a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotnine import ggtitle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "2ba51855-f434-4aa4-9dbd-0e6026fad873",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    ggplot(rews_df, aes(x='rew', fill='strat')) \n",
+    "    + geom_density(alpha=0.6) \n",
+    "    + ggtitle(f\"ppo mean: {np.mean(ppo_df.rew): .2f}, c. act. mean: {np.mean(ca_df.rew): .2f}\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47dd1494-58b3-4391-b021-32c6b78fcc14",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1880ffd9-e552-4e29-a282-b2651ff07800",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/optimal-fixed-policy.ipynb b/notebooks/optimal-fixed-policy.ipynb
index 5b1b806..08e488a 100644
--- a/notebooks/optimal-fixed-policy.ipynb
+++ b/notebooks/optimal-fixed-policy.ipynb
@@ -10,71 +10,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 1,
    "id": "f15d4b8e-ef57-4bce-899b-89bb32d396f6",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Obtaining file:///home/rstudio/rl4fisheries\n",
-      "  Installing build dependencies ... \u001b[?25ldone\n",
-      "\u001b[?25h  Checking if build backend supports build_editable ... \u001b[?25ldone\n",
-      "\u001b[?25h  Getting requirements to build editable ... \u001b[?25ldone\n",
-      "\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n",
-      "\u001b[?25h  Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25hRequirement already satisfied: gymnasium in /opt/venv/lib/python3.10/site-packages (from rl4fisheries==1.0.0) (0.28.1)\n",
-      "Requirement already satisfied: numpy in /opt/venv/lib/python3.10/site-packages (from rl4fisheries==1.0.0) (1.26.4)\n",
-      "Requirement already satisfied: matplotlib in /opt/venv/lib/python3.10/site-packages (from rl4fisheries==1.0.0) (3.8.2)\n",
-      "Requirement already satisfied: typing in /opt/venv/lib/python3.10/site-packages (from rl4fisheries==1.0.0) (3.7.4.3)\n",
-      "Requirement already satisfied: jax-jumpy>=1.0.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4fisheries==1.0.0) (1.0.0)\n",
-      "Requirement already satisfied: cloudpickle>=1.2.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4fisheries==1.0.0) (3.0.0)\n",
-      "Requirement already satisfied: typing-extensions>=4.3.0 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4fisheries==1.0.0) (4.9.0)\n",
-      "Requirement already satisfied: farama-notifications>=0.0.1 in /opt/venv/lib/python3.10/site-packages (from gymnasium->rl4fisheries==1.0.0) (0.0.4)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (1.2.0)\n",
-      "Requirement already satisfied: cycler>=0.10 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (0.12.1)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (4.48.1)\n",
-      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (1.4.5)\n",
-      "Requirement already satisfied: packaging>=20.0 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (23.2)\n",
-      "Requirement already satisfied: pillow>=8 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (10.2.0)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (3.1.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /opt/venv/lib/python3.10/site-packages (from matplotlib->rl4fisheries==1.0.0) (2.8.2)\n",
-      "Requirement already satisfied: six>=1.5 in /opt/venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->rl4fisheries==1.0.0) (1.16.0)\n",
-      "Building wheels for collected packages: rl4fisheries\n",
-      "  Building editable for rl4fisheries (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for rl4fisheries: filename=rl4fisheries-1.0.0-0.editable-py3-none-any.whl size=2176 sha256=aebb65ca4f07d99d588c7fb0de18b7cdbb9aff0bb29ab44fe3dd315eb92caf22\n",
-      "  Stored in directory: /tmp/pip-ephem-wheel-cache-t5m_i4it/wheels/d3/ce/fe/d5af67bb4edf309f6a59d59140b2b78d5a336b2ad4b93a1fb4\n",
-      "Successfully built rl4fisheries\n",
-      "Installing collected packages: rl4fisheries\n",
-      "  Attempting uninstall: rl4fisheries\n",
-      "    Found existing installation: rl4fisheries 1.0.0\n",
-      "    Uninstalling rl4fisheries-1.0.0:\n",
-      "      Successfully uninstalled rl4fisheries-1.0.0\n",
-      "Successfully installed rl4fisheries-1.0.0\n",
-      "Note: you may need to restart the kernel to use updated packages.\n",
-      "Requirement already satisfied: scikit-optimize in /opt/venv/lib/python3.10/site-packages (0.9.0)\n",
-      "Requirement already satisfied: joblib>=0.11 in /opt/venv/lib/python3.10/site-packages (from scikit-optimize) (1.3.2)\n",
-      "Requirement already satisfied: pyaml>=16.9 in /opt/venv/lib/python3.10/site-packages (from scikit-optimize) (23.12.0)\n",
-      "Requirement already satisfied: numpy>=1.13.3 in /opt/venv/lib/python3.10/site-packages (from scikit-optimize) (1.26.4)\n",
-      "Requirement already satisfied: scipy>=0.19.1 in /opt/venv/lib/python3.10/site-packages (from scikit-optimize) (1.12.0)\n",
-      "Requirement already satisfied: scikit-learn>=0.20.0 in /opt/venv/lib/python3.10/site-packages (from scikit-optimize) (1.4.0)\n",
-      "Requirement already satisfied: PyYAML in /opt/venv/lib/python3.10/site-packages (from pyaml>=16.9->scikit-optimize) (6.0.1)\n",
-      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/venv/lib/python3.10/site-packages (from scikit-learn>=0.20.0->scikit-optimize) (3.2.0)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%pip install -e ..\n",
+    "# %pip install -e ..\n",
     "# %pip install scikit-optimize"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 1,
    "id": "8a7920aa-5e69-4690-be6d-f03308ddf449",
    "metadata": {},
    "outputs": [],
@@ -83,7 +32,10 @@
     "from skopt import gp_minimize, gbrt_minimize\n",
     "import polars as pl\n",
     "import numpy as np\n",
-    "from plotnine import ggplot, aes, geom_point, geom_ribbon\n"
+    "from plotnine import ggplot, aes, geom_point, geom_ribbon\n",
+    "\n",
+    "import pandas as pd\n",
+    "from skopt.plots import plot_objective, plot_convergence\n"
    ]
   },
   {
@@ -96,14 +48,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 2,
    "id": "51c9e6d9-9299-4296-b647-cf498b0b92cb",
    "metadata": {},
    "outputs": [],
    "source": [
     "class fixed_effort:\n",
-    "    def __init__(self, action):\n",
-    "        self.effort = np.array(action, dtype=np.float32)\n",
+    "    def __init__(self, effort):\n",
+    "        self.effort = np.array(effort, dtype=np.float32)\n",
     "\n",
     "    def predict(self, observation, **kwargs):\n",
     "        action = self.effort * 2 - 1\n",
@@ -114,27 +66,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 3,
    "id": "cdeb4bd8-9620-4c4f-829d-a3a2c0e8dfd3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([-0.4943695 , -0.48672426, -0.8089408 ], dtype=float32),\n",
-       " 0.250531405210495,\n",
+       "(array([-0.88323575, -0.9932028 , -0.81746125], dtype=float32),\n",
+       " -6.324660778045655e-05,\n",
        " False,\n",
        " False,\n",
        " {})"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "env = Caribou()\n",
+    "am = {\"current\": 1, \"full_rest\": 0.5}\n",
+    "ab = {\"current\": 5, \"full_rest\": 1}\n",
+    "\n",
+    "parameters = {\n",
+    "    \"r_m\": np.float32(0.5),\n",
+    "    \"r_b\": np.float32(0.45),\n",
+    "    #\n",
+    "    \"alpha_mm\": np.float32(0.1),\n",
+    "    \"alpha_bb\": np.float32(0.1),\n",
+    "    \"alpha_bm\": np.float32(0.05),\n",
+    "    \"alpha_mb\": np.float32(0.05),\n",
+    "    #\n",
+    "    \"a_M\": am[\"current\"],\n",
+    "    \"a_B\": ab[\"current\"],\n",
+    "    # \"a_M\": 1,\n",
+    "    # \"a_B\": 2,\n",
+    "    #\n",
+    "    \"K_m\": np.float32(1.1),\n",
+    "    \"K_b\": np.float32(0.40),\n",
+    "    #\n",
+    "    \"h_B\": np.float32(0.031),\n",
+    "    \"h_M\": np.float32(0.31),\n",
+    "    #\n",
+    "    \"x\": np.float32(2),\n",
+    "    \"u\": np.float32(1),\n",
+    "    \"d\": np.float32(0.3),\n",
+    "    #\n",
+    "    \"sigma_M\": np.float32(0.2),\n",
+    "    \"sigma_B\": np.float32(0.25),\n",
+    "    \"sigma_W\": np.float32(0.2),\n",
+    "    # \"sigma_M\": np.float32(0.),\n",
+    "    # \"sigma_B\": np.float32(0.),\n",
+    "    # \"sigma_W\": np.float32(0.),\n",
+    "    \"additive_sigma\": np.float32(0.005),\n",
+    "}\n",
+    "\n",
+    "\n",
+    "config = {\n",
+    "    'parameters': parameters,\n",
+    "    'initial_pop': np.float32([0.572079, 0.025453, 0.911731]),\n",
+    "    # ^ use convergence point for null action and no stochasticity\n",
+    "}\n",
+    "\n",
+    "env = Caribou(config=config)\n",
     "obs = env.reset()\n",
     "action, _ = pacifist.predict(obs)\n",
     "env.step(action)"
@@ -154,11 +149,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 4,
    "id": "6fa4681c-fbca-4ade-a8ab-e021cd9d07e5",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import ray\n",
+    "\n",
+    "@ray.remote\n",
     "def gen_ep_rew(manager, env):\n",
     "    episode_reward = 0.0\n",
     "    observation, _ = env.reset()\n",
@@ -170,8 +168,12 @@
     "            break\n",
     "    return episode_reward\n",
     "\n",
-    "def gather_stats(manager, env, N=10):\n",
-    "    results = [gen_ep_rew(manager, env) for _ in range(N)]\n",
+    "def gather_stats(manager, env, N=200):\n",
+    "    results = ray.get(\n",
+    "        [gen_ep_rew.remote(manager, env) for _ in range(N)]\n",
+    "    )\n",
+    "    ray.shutdown()\n",
+    "    #\n",
     "    y = np.mean(results)\n",
     "    sigma = np.std(results)\n",
     "    ymin = y - sigma\n",
@@ -181,23 +183,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 9,
    "id": "f3ddd3b8-6696-4f0d-a9a1-c6d722deb3b1",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:25:38,232\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "(23.70720968544483, 5.766838293769911, 41.64758107711975)"
+       "(-5.567889918454439, -6.924977668806896, -4.210802168101981)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gen_ep_rew(pacifist, env)\n",
     "gather_stats(pacifist, env)"
    ]
   },
@@ -213,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 5,
    "id": "d876df99-b2ab-49ab-aaa0-3b545cc7ae4b",
    "metadata": {},
    "outputs": [],
@@ -226,8 +236,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "812edc32-f0f9-4ff4-9792-77acf6962179",
+   "execution_count": 12,
+   "id": "049ba8a4-b81f-4b31-815b-be7970cba35d",
    "metadata": {
     "scrolled": true
    },
@@ -236,57 +246,1049 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2min 42s, sys: 9min 48s, total: 12min 31s\n",
-      "Wall time: 1min 40s\n"
+      "Iteration No: 1 started. Evaluating function at random point.\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "(-192.28646437703884, [0.17790704682764627, 0.061024282615602])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "res = gp_minimize(g, [(0.0, 0.3), (0, 0.3)], n_calls = 300)\n",
-    "res.fun, res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "4c5c2ec8-f61b-4dae-bc1b-ba70310a694b",
-   "metadata": {},
-   "outputs": [
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:26:41,368\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3min 41s, sys: 644 ms, total: 3min 42s\n",
-      "Wall time: 3min 40s\n"
+      "Iteration No: 1 ended. Evaluation done at random point.\n",
+      "Time taken: 6.9099\n",
+      "Function value obtained: 8.3944\n",
+      "Current minimum: 8.3944\n",
+      "Iteration No: 2 started. Evaluating function at random point.\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "(-183.45138428616946, [0.1625976286665279, 0.05916838814404951])"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "res = gbrt_minimize(g, [(0.0, 0.3), (0, 0.3)], n_calls = 300)\n",
-    "res.fun, res.x"
-   ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:26:48,140\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 2 ended. Evaluation done at random point.\n",
+      "Time taken: 6.6931\n",
+      "Function value obtained: 8.5189\n",
+      "Current minimum: 8.3944\n",
+      "Iteration No: 3 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:26:54,954\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 3 ended. Evaluation done at random point.\n",
+      "Time taken: 6.8978\n",
+      "Function value obtained: 7.8332\n",
+      "Current minimum: 7.8332\n",
+      "Iteration No: 4 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:01,789\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 4 ended. Evaluation done at random point.\n",
+      "Time taken: 6.5554\n",
+      "Function value obtained: 6.4482\n",
+      "Current minimum: 6.4482\n",
+      "Iteration No: 5 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:08,324\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 5 ended. Evaluation done at random point.\n",
+      "Time taken: 6.7635\n",
+      "Function value obtained: 7.8851\n",
+      "Current minimum: 6.4482\n",
+      "Iteration No: 6 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:15,202\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 6 ended. Evaluation done at random point.\n",
+      "Time taken: 6.6887\n",
+      "Function value obtained: 7.4570\n",
+      "Current minimum: 6.4482\n",
+      "Iteration No: 7 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:21,924\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 7 ended. Evaluation done at random point.\n",
+      "Time taken: 6.7629\n",
+      "Function value obtained: 7.4776\n",
+      "Current minimum: 6.4482\n",
+      "Iteration No: 8 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:28,673\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 8 ended. Evaluation done at random point.\n",
+      "Time taken: 6.6721\n",
+      "Function value obtained: 8.2757\n",
+      "Current minimum: 6.4482\n",
+      "Iteration No: 9 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:35,228\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 9 ended. Evaluation done at random point.\n",
+      "Time taken: 6.4267\n",
+      "Function value obtained: 3.4761\n",
+      "Current minimum: 3.4761\n",
+      "Iteration No: 10 started. Evaluating function at random point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:41,699\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 10 ended. Evaluation done at random point.\n",
+      "Time taken: 6.8884\n",
+      "Function value obtained: 7.7584\n",
+      "Current minimum: 3.4761\n",
+      "Iteration No: 11 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:48,590\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 11 ended. Search finished for the next optimal point.\n",
+      "Time taken: 6.9206\n",
+      "Function value obtained: 7.7113\n",
+      "Current minimum: 3.4761\n",
+      "Iteration No: 12 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:27:55,490\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 12 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.0718\n",
+      "Function value obtained: 3.6899\n",
+      "Current minimum: 3.4761\n",
+      "Iteration No: 13 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:02,737\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 13 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3528\n",
+      "Function value obtained: 8.1123\n",
+      "Current minimum: 3.4761\n",
+      "Iteration No: 14 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:10,032\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 14 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.2734\n",
+      "Function value obtained: 6.6178\n",
+      "Current minimum: 3.4761\n",
+      "Iteration No: 15 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:17,313\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 15 ended. Search finished for the next optimal point.\n",
+      "Time taken: 6.9263\n",
+      "Function value obtained: 3.8129\n",
+      "Current minimum: 3.4761\n",
+      "Iteration No: 16 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:24,228\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 16 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.4502\n",
+      "Function value obtained: 3.3117\n",
+      "Current minimum: 3.3117\n",
+      "Iteration No: 17 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:31,652\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 17 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.2443\n",
+      "Function value obtained: 2.7624\n",
+      "Current minimum: 2.7624\n",
+      "Iteration No: 18 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:38,831\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 18 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.2384\n",
+      "Function value obtained: 3.4623\n",
+      "Current minimum: 2.7624\n",
+      "Iteration No: 19 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:46,141\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 19 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.2537\n",
+      "Function value obtained: 1.8911\n",
+      "Current minimum: 1.8911\n",
+      "Iteration No: 20 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:28:53,438\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 20 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3900\n",
+      "Function value obtained: 2.7528\n",
+      "Current minimum: 1.8911\n",
+      "Iteration No: 21 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:00,823\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 21 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.0239\n",
+      "Function value obtained: 1.7782\n",
+      "Current minimum: 1.7782\n",
+      "Iteration No: 22 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:07,728\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 22 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.8995\n",
+      "Function value obtained: 1.9291\n",
+      "Current minimum: 1.7782\n",
+      "Iteration No: 23 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:15,649\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 23 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.1420\n",
+      "Function value obtained: 2.1447\n",
+      "Current minimum: 1.7782\n",
+      "Iteration No: 24 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:22,806\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 24 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.2428\n",
+      "Function value obtained: 1.7375\n",
+      "Current minimum: 1.7375\n",
+      "Iteration No: 25 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:30,149\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 25 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.1471\n",
+      "Function value obtained: 1.9571\n",
+      "Current minimum: 1.7375\n",
+      "Iteration No: 26 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:37,189\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 26 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9695\n",
+      "Function value obtained: 1.7196\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 27 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:45,309\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 27 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.4276\n",
+      "Function value obtained: 1.9116\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 28 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:52,623\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 28 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.0555\n",
+      "Function value obtained: 5.8750\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 29 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:29:59,767\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 29 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3063\n",
+      "Function value obtained: 1.7385\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 30 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:06,984\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 30 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.0246\n",
+      "Function value obtained: 1.7629\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 31 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:14,010\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 31 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0223\n",
+      "Function value obtained: 1.8089\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 32 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:22,140\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 32 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3475\n",
+      "Function value obtained: 1.8630\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 33 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:29,471\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 33 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.1381\n",
+      "Function value obtained: 2.2714\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 34 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:36,617\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 34 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.0736\n",
+      "Function value obtained: 5.7942\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 35 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:43,643\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 35 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.1881\n",
+      "Function value obtained: 4.5248\n",
+      "Current minimum: 1.7196\n",
+      "Iteration No: 36 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:50,814\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 36 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3308\n",
+      "Function value obtained: 1.6878\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 37 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:30:58,177\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 37 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.1597\n",
+      "Function value obtained: 8.8895\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 38 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:05,404\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 38 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1257\n",
+      "Function value obtained: 4.6135\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 39 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:13,449\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 39 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.2257\n",
+      "Function value obtained: 7.7961\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 40 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:20,679\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 40 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0782\n",
+      "Function value obtained: 7.7090\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 41 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:28,752\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 41 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3119\n",
+      "Function value obtained: 1.7442\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 42 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:36,168\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 42 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3256\n",
+      "Function value obtained: 7.5428\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 43 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:44,425\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 43 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.2707\n",
+      "Function value obtained: 8.8123\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 44 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:51,663\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 44 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.0775\n",
+      "Function value obtained: 7.2416\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 45 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:31:59,751\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 45 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.9931\n",
+      "Function value obtained: 4.1048\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 46 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:32:07,760\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 46 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.3357\n",
+      "Function value obtained: 10.8838\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 47 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:32:15,206\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 47 ended. Search finished for the next optimal point.\n",
+      "Time taken: 7.4287\n",
+      "Function value obtained: 6.6997\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 48 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:32:22,631\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 48 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.4265\n",
+      "Function value obtained: 5.9701\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 49 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:32:31,077\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 49 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1849\n",
+      "Function value obtained: 6.6603\n",
+      "Current minimum: 1.6878\n",
+      "Iteration No: 50 started. Searching for the next optimal point.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-16 22:32:39,149\tINFO worker.py:1749 -- Started a local Ray instance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration No: 50 ended. Search finished for the next optimal point.\n",
+      "Time taken: 8.1697\n",
+      "Function value obtained: 8.1234\n",
+      "Current minimum: 1.6878\n",
+      "CPU times: user 7min 21s, sys: 9min 19s, total: 16min 41s\n",
+      "Wall time: 6min 5s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(1.6878340780852437, [0.018989021076692904, 0.3169121824404405])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "res = gp_minimize(g, [(0.0, 1.0), (0.0, 1.0)], n_calls = 50, verbose=True)\n",
+    "res.fun, res.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "61e8fc40-6157-4a96-9fad-74ebc4d3f7b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_objective(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c5c2ec8-f61b-4dae-bc1b-ba70310a694b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%time\n",
+    "# res = gbrt_minimize(g, [(0.0, 0.3), (0, 0.3)], n_calls = 300)\n",
+    "# res.fun, res.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2346c45-e588-45ab-b7a8-5677a60ac8e9",
+   "metadata": {},
+   "source": [
+    "## Test solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "01b2030d-e7df-4243-9203-d602e74aa9a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Caribou' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m carib \u001b[38;5;241m=\u001b[39m \u001b[43mCaribou\u001b[49m(config\u001b[38;5;241m=\u001b[39mconfig)\n\u001b[1;32m      3\u001b[0m null_action \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39mones(\u001b[38;5;241m2\u001b[39m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m      4\u001b[0m high_action \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mones(\u001b[38;5;241m2\u001b[39m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat32)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'Caribou' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "carib = Caribou(config=config)\n",
+    "\n",
+    "null_action = - np.ones(2, dtype=np.float32)\n",
+    "high_action = 0.5 * np.ones(2, dtype=np.float32)\n",
+    "all_wolves = np.float32([0.0, 0.5])\n",
+    "all_moose = np.float32([0.5, 0.0])\n",
+    "# an_effort = np.float32(res.x)\n",
+    "an_effort = np.float32([0.018989021076692904, 0.3169121824404405])\n",
+    "\n",
+    "an_action = 2 * an_effort - 1\n",
+    "\n",
+    "Ms, Bs, Ws, ts, rews = [], [], [], [], []\n",
+    "obs, _ = carib.reset()\n",
+    "ep_rew=0\n",
+    "#\n",
+    "pop = carib.population_units()\n",
+    "Ms.append(pop[0])\n",
+    "Bs.append(pop[1])\n",
+    "Ws.append(pop[2])\n",
+    "ts.append(0)\n",
+    "rews.append(ep_rew)\n",
+    "#\n",
+    "for t in range(carib.Tmax):\n",
+    "    ts.append(t+1)\n",
+    "    obs, rew, term, trunc, info = carib.step(an_action)\n",
+    "    pop = carib.population_units()\n",
+    "    Ms.append(pop[0])\n",
+    "    Bs.append(pop[1])\n",
+    "    Ws.append(pop[2])\n",
+    "    ep_rew += rew\n",
+    "    rews.append(ep_rew)\n",
+    "    if term or trunc:\n",
+    "        break\n",
+    "    \n",
+    "\n",
+    "ep = pd.DataFrame({\n",
+    "    't': ts,\n",
+    "    'm': Ms,\n",
+    "    'b': Bs,\n",
+    "    'w': Ws,\n",
+    "})\n",
+    "\n",
+    "ep.plot(x='t', title=f'{ep_rew}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81150bac-2bb1-4a3d-bb92-390188ee85ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/scripts/train.py b/scripts/train.py
index cad37b3..ad49ef4 100644
--- a/scripts/train.py
+++ b/scripts/train.py
@@ -7,17 +7,27 @@
 
 import rl4caribou
 
+## normalizing work directory 
+#
+import os
+abs_filepath = os.path.abspath(args.file)
+
+# change directory to script's directory (since io uses relative paths)
+abspath = os.path.abspath(__file__)
+dname = os.path.dirname(abspath)
+os.chdir(dname)
+
 # training
 #
 from rl4caribou.utils import sb3_train    
-model_save_id, train_options = sb3_train(args.file, progress_bar=args.progress_bar)
+model_save_id, train_options = sb3_train(abs_filepath, progress_bar=args.progress_bar)
 
 # hugging face
 #
 if 'repo' in train_options:
     from rl4caribou.utils import upload_to_hf
     try:
-        upload_to_hf(args.file, "sb3/"+args.file, repo=train_options['repo'])
+        upload_to_hf(abs_filepath, "sb3/"+args.file, repo=train_options['repo'])
         upload_to_hf(model_save_id, "sb3/"+model_save_id+".zip", repo=train_options['repo'])
     except:
         print("Couldn't upload to hf!")
\ No newline at end of file
diff --git a/src/rl4caribou/agents/const_action.py b/src/rl4caribou/agents/const_action.py
index 464af2f..ec09888 100644
--- a/src/rl4caribou/agents/const_action.py
+++ b/src/rl4caribou/agents/const_action.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 class constAction:
-    def __init__(self, mortality_vec=np.zeros(2, dtype=np.float32), env = None, **kwargs):
+    def __init__(self, mortality_vec=np.zeros(3, dtype=np.float32), env = None, **kwargs):
         #
         # preprocess
         if isinstance(mortality_vec, list):
diff --git a/src/rl4caribou/envs/caribou.py b/src/rl4caribou/envs/caribou.py
index 99d9be7..92385ec 100644
--- a/src/rl4caribou/envs/caribou.py
+++ b/src/rl4caribou/envs/caribou.py
@@ -6,46 +6,59 @@
 def dynamics(pop, effort, harvest_fn, p, timestep=1):
     pop = harvest_fn(pop, effort)
     M, B, W = pop[0], pop[1], pop[2] # moose, caribou, wolf
-    denominator  = (1 + B**p['x'] * p['h_B'] * p['a_B'] + M**p['x'] * p['h_M'] * p['a_M'])
+    p['a_B(t)'] = p['a_B'] * (1 + min(3, 6 * timestep / 800))
+    # print(p['a_B(t)'])
     
-    return np.float32([
-        M + M * (
-            p['r_m'] * (1 - p['alpha_mm'] * M / p['K_m'])
-            - M**(p['x'] - 1) * W * p['a_M'] / denominator
-            - p['r_m'] * p['alpha_mb'] * B / p['K_m']
-            + p['sigma_M'] * np.random.normal()
-        ),
-        #
-        B + B * (
-            p['r_b']  * (1 - p['alpha_bb'] * B / p['K_b'])
-            -  B**(p['x']-1) * W * p['a_B']  / denominator
-            - p['r_b'] * p['alpha_bm'] * M / p['K_b']
-            + p['sigma_B'] * np.random.normal()
-        ),
-        #
-        W + W * (
-            B**p['x'] * p['a_B'] /  denominator
-            + M**p['x'] * p['a_M'] * p['u'] / denominator
-            - p['d']
-            + p['sigma_W'] * np.random.normal()
-        ),
-    ])
+    denominator  = (1 + B**p['x'] * p['h_B'] * p['a_B(t)'] + M**p['x'] * p['h_M'] * p['a_M'])
+
+    B_zero = 0 if B==0 else 1
+
+    zero_B_mask = np.float32([1, B_zero, 1])
+    # wolf and moose could randomly move into the habitat from elsewhere
+    
+    return np.clip(
+        zero_B_mask * np.float32([
+            M + 0.2 * M * (
+                p['r_m'] * (1 - p['alpha_mm'] * M / p['K_m'])
+                - M**(p['x'] - 1) * W * p['a_M'] / denominator
+                - p['r_m'] * p['alpha_mb'] * B / p['K_m']
+                + p['sigma_M'] * np.random.normal()
+            ) + p['additive_sigma'] * np.random.normal(),
+            #
+            B + 0.2 * B * (
+                p['r_b']  * (1 - p['alpha_bb'] * B / p['K_b'])
+                -  B**(p['x']-1) * W * p['a_B(t)']  / denominator
+                - p['r_b'] * p['alpha_bm'] * M / p['K_b']
+                + p['sigma_B'] * np.random.normal()
+            ) + p['additive_sigma'] * np.random.normal(),
+            #
+            W + 0.2 *W * (
+                B**(p['x']) * p['a_B'] /  denominator
+                + M**(p['x']) * p['a_M'] * p['u'] / denominator
+                - p['d']
+                + p['sigma_W'] * np.random.normal()
+            ) + p['additive_sigma'] * np.random.normal(), 
+        ]),
+        a_min = np.float32([0,0,0]),
+        a_max=None,
+    )
 
 
 ##
-## Param vals taken from https://doi.org/10.1016/j.ecolmodel.2019.108891
+## Param vals from notebooks/discrete_time.ipynmb experiments
 ##
-am = {"current": 15.32, "full_rest": 11.00}
-ab = {"current": 51.45, "full_rest": 26.39}
+
+am = {"current": 1, "full_rest": 0.5}
+ab = {"current": 5, "full_rest": 1}
 
 parameters = {
-    "r_m": np.float32(0.39),
-    "r_b": np.float32(0.30),
+    "r_m": np.float32(0.5),
+    "r_b": np.float32(0.45),
     #
-    "alpha_mm": np.float32(1),
-    "alpha_bb": np.float32(1),
-    "alpha_bm": np.float32(1),
-    "alpha_mb": np.float32(1),
+    "alpha_mm": np.float32(0.1),
+    "alpha_bb": np.float32(0.1),
+    "alpha_bm": np.float32(0.05),
+    "alpha_mb": np.float32(0.05),
     #
     "a_M": am["current"],
     "a_B": ab["current"],
@@ -53,16 +66,17 @@ def dynamics(pop, effort, harvest_fn, p, timestep=1):
     "K_m": np.float32(1.1),
     "K_b": np.float32(0.40),
     #
-    "h_M": np.float32(0.112),
-    "h_B": np.float32(0.112),
+    "h_B": np.float32(0.031),
+    "h_M": np.float32(0.31),
     #
     "x": np.float32(2),
     "u": np.float32(1),
-    "d": np.float32(1),
+    "d": np.float32(0.3),
     #
-    "sigma_M": np.float32(0.1),
-    "sigma_B": np.float32(0.1),
-    "sigma_W": np.float32(0.1),
+    "sigma_M": np.float32(0.2),
+    "sigma_B": np.float32(0.25),
+    "sigma_W": np.float32(0.2),
+    "additive_sigma": np.float32(0.005),
 }
 
 
@@ -70,19 +84,32 @@ def dynamics(pop, effort, harvest_fn, p, timestep=1):
 ## Harvest, utility
 ##
 def harvest(pop, effort):
-    q0 = 0.5  # catchability coefficients -- erradication is impossible
-    q2 = 0.5
+    q0 = 1  # catchability coefficients -- erradication is impossible
+    q2 = 1
     pop[0] = pop[0] * (1 - effort[0] * q0)  # pop 0, moose
     pop[2] = pop[2] * (1 - effort[1] * q2)  # pop 2, wolves
     return pop
 
 
-def utility(pop, effort):
-    benefits = 0.5 * pop[1]  # benefit from Caribou
-    costs = 0.00001 * (effort[0] + effort[1])  # cost to culling
-    if np.any(pop <= 0.01):
-        benefits -= 1
-    return benefits - costs
+def utility(pop, effort, env):
+    benefit_vec = [0.2, 0.5, 0.2]
+    benefits = sum(benefit_vec * pop) # benefit from populations
+    costs = 0.1 * effort[0] + 0.2 * effort[1]  # cost to culling
+
+    thresholds = [env.initial_pop[0], 0.1, env.initial_pop[2]]
+    for i, pop_i in enumerate(pop):
+        if pop_i < thresholds[i]:
+            benefits -= 5 * (thresholds[i] - pop_i)
+        if (pop_i == 0) and (i==1):
+            # caribou crash
+            benefits -= 10
+        if (pop_i == 0) and not (i==1):
+            # other crash
+            benefits -= 5
+    return 0.001 * (benefits - costs)
+
+def triv_observe(state):
+    return state
 
 class Caribou(gym.Env):
     """A 3-species ecosystem model with two control actions"""
@@ -93,18 +120,21 @@ def __init__(self, config=None):
         ## these parameters may be specified in config
         self.Tmax = config.get("Tmax", 800)
         self.max_episode_steps = self.Tmax
-        self.threshold = config.get("threshold", np.float32(1e-4))
+        self.threshold = config.get("threshold", np.float32(1e-3))
         self.init_sigma = config.get("init_sigma", np.float32(1e-3))
         self.training = config.get("training", True)
-        self.initial_pop = config.get("initial_pop", np.ones(3, dtype=np.float32))
+        self.initial_pop = config.get(
+            "initial_pop", 
+            np.float32([0.572079, 0.025453, 0.911731]),
+        )
         self.parameters = config.get("parameters", parameters)
         self.dynamics = config.get("dynamics", dynamics)
         self.harvest = config.get("harvest", harvest)
         self.utility = config.get("utility", utility)
         self.observe = config.get(
-            "observe", lambda state: state
+            "observe", triv_observe
         )  # default to perfectly observed case
-        self.bound = 2
+        self.bound = 10
 
         self.action_space = gym.spaces.Box(
             np.array([-1, -1], dtype=np.float32),
@@ -120,12 +150,11 @@ def __init__(self, config=None):
 
     def reset(self, *, seed=None, options=None):
         self.timestep = 0
-        self.initial_pop += np.multiply(
-            self.initial_pop, np.float32(self.init_sigma * np.random.normal(size=3))
-        )
+        # self.initial_pop = self.initial_pop * (1 + self.init_sigma * np.random.normal(size=3))
         self.state = self.state_units(self.initial_pop)
         info = {}
-        return self.observe(self.state), info
+        observation = self.observe(self.state)
+        return observation, info
 
     def step(self, action):
         action = np.clip(action, self.action_space.low, self.action_space.high)
@@ -133,7 +162,7 @@ def step(self, action):
         effort = (action + 1.0) / 2
 
         # harvest and recruitment
-        reward = self.utility(pop, effort)
+        reward = self.utility(pop, effort, self)
         nextpop = self.dynamics(
             pop, effort, self.harvest, self.parameters, self.timestep
         )
@@ -141,10 +170,10 @@ def step(self, action):
         self.timestep += 1
         terminated = bool(self.timestep > self.Tmax)
 
-        # in training mode only: punish for population collapse
-        if any(pop <= self.threshold) and self.training:
-            terminated = True
-            reward -= 50 / self.timestep
+        # # in training mode only: punish for population collapse
+        # if any(pop <= self.threshold) and self.training:
+        #     terminated = True
+        #     reward -= 100 / self.timestep
 
         self.state = self.state_units(nextpop)  # transform into [-1, 1] space
         observation = self.observe(self.state)  # same as self.state
diff --git a/src/rl4caribou/envs/caribou_ode.py b/src/rl4caribou/envs/caribou_ode.py
index b282c82..2cdaa75 100644
--- a/src/rl4caribou/envs/caribou_ode.py
+++ b/src/rl4caribou/envs/caribou_ode.py
@@ -16,7 +16,9 @@ def dynamics_scipy(pop, effort, p, timestep, singularities):
         ) *
         np.random.normal(size=3)
     )
-    return odeint(ode_func, y0, t_interval, args=(effort, p), tcrit=singularities)[1] + timestep_randomness * dt
+    new_pop = odeint(ode_func, y0, t_interval, args=(effort, p), tcrit=singularities)[1]
+    
+    return new_pop + timestep_randomness * dt
 
 def ode_func(y, t, effort, p):
     M, B, W = y
@@ -114,11 +116,18 @@ def harvest(pop, effort):
 
 def utility(pop, effort):
     benefits = 1 * pop[1]  # benefit from Caribou
-    costs = 0.1 * (effort[0] + effort[1]) + 0.1 * effort[2]  # cost to culling + cost of restoring
-    if np.any(pop <= [0.03,  0.07, 1e-4]):
+    costs = 0.1 * (effort[0] + effort[1]) + 0.4 * effort[2]  # cost to culling + cost of restoring
+    if np.any(pop <= [0.05,  0.01, 0.001]):
         benefits -= 1
     return benefits - costs
 
+def observe_3pop_restoration(env):
+    rest_obs = -1 + 2 * (
+        (env.current_ab - env.parameters["a_B"]) 
+        / (env.current_ab - env.restored_ab)
+    )
+    return np.float32([*env.state, rest_obs])
+
 class CaribouScipy(gym.Env):
     """A 3-species ecosystem model with two control actions"""
 
@@ -131,7 +140,7 @@ def __init__(self, config=None):
         self.threshold = config.get("threshold", np.float32(1e-4))
         self.init_sigma = config.get("init_sigma", np.float32(1e-3))
         self.training = config.get("training", True)
-        self.initial_pop = config.get("initial_pop", np.float32([0.3, 0.15, 0.05]))
+        self.initial_pop = np.float32(config.get("initial_pop", [0.268, 0.023, 0.079]))
         #
         self.current_am = 15.32
         self.restored_am = 11.00
@@ -170,7 +179,7 @@ def __init__(self, config=None):
         self.harvest = config.get("harvest", harvest)
         self.utility = config.get("utility", utility)
         self.observe = config.get(
-            "observe", lambda state: state
+            "observe", observe_3pop_restoration
         )  # default to perfectly observed case
         self.bound = 2
 
@@ -180,8 +189,8 @@ def __init__(self, config=None):
             dtype=np.float32,
         )
         self.observation_space = gym.spaces.Box(
-            np.array([-1, -1, -1], dtype=np.float32),
-            np.array([1, 1, 1], dtype=np.float32),
+            np.array([-1, -1, -1, -1], dtype=np.float32),
+            np.array([1, 1, 1, 1], dtype=np.float32),
             dtype=np.float32,
         )
         self.reset(seed=config.get("seed", None))
@@ -193,7 +202,7 @@ def reset(self, *, seed=None, options=None):
         )
         self.state = self.state_units(self.true_initial_pop)
         info = {}
-        return self.observe(self.state), info
+        return self.observe(self), info
 
     def step(self, action):
         action = np.clip(action, self.action_space.low, self.action_space.high)
@@ -216,7 +225,7 @@ def step(self, action):
         truncated = bool(self.timestep > self.Tmax) # or bool(any(nextpop < 1e-7))
         
         self.state = self.state_units(nextpop)  # transform into [-1, 1] space
-        observation = self.observe(self.state)  # same as self.state
+        observation = self.observe(self)  # same as self.state
         return observation, reward, False, truncated, {}
 
     def state_units(self, pop):
diff --git a/src/rl4caribou/utils/sb3.py b/src/rl4caribou/utils/sb3.py
index 67287f1..e581eb9 100644
--- a/src/rl4caribou/utils/sb3.py
+++ b/src/rl4caribou/utils/sb3.py
@@ -36,13 +36,58 @@ def algorithm(algo):
         'tqc': TQC,
     }
     return algos[algo]
-
 def sb3_train(config_file, **kwargs):
     with open(config_file, "r") as stream:
         options = yaml.safe_load(stream)
         options = {**options, **kwargs}
         # updates / expands on yaml options with optional user-provided input
 
+    if 'additional_imports' in options:
+        import importlib 
+        for module in options['additional_imports']:
+            print(f"importing {module}")
+            module = importlib.import_module(module)
+            globals()[module.__name__] = module
+    
+    if "n_envs" in options:
+        env = make_vec_env(
+            options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]}
+        )
+    else:
+        env = gym.make(options["env_id"])
+
+    if (
+        'policy_kwargs' in options['algo_config'] and 
+        isinstance(options['algo_config']['policy_kwargs'], str)
+    ):
+        options['algo_config']['policy_kwargs'] = eval(options['algo_config']['policy_kwargs'])
+    
+    ALGO = algorithm(options["algo"])
+    if "id" in options:
+        options["id"] = "-" + options["id"]
+    model_id = options["algo"] + "-" + options["env_id"]  + options.get("id", "")
+    save_id = os.path.join(options["save_path"], model_id)
+
+    model = ALGO(
+        env=env,
+        **options['algo_config']
+    )
+
+    progress_bar = options.get("progress_bar", False)
+    model.learn(total_timesteps=options["total_timesteps"], tb_log_name=model_id, progress_bar=progress_bar)
+
+    os.makedirs(options["save_path"], exist_ok=True)
+    model.save(save_id)
+    print(f"Saved {options['algo']} model at {save_id}")
+    
+    return save_id, options
+    
+def sb3_train_old(config_file, **kwargs):
+    with open(config_file, "r") as stream:
+        options = yaml.safe_load(stream)
+        options = {**options, **kwargs}
+        # updates / expands on yaml options with optional user-provided input
+
     if "n_envs" in options:
         env = make_vec_env(
             options["env_id"], options["n_envs"], env_kwargs={"config": options["config"]}